WO2018098588A1

WO2018098588A1 - Computer systems for and methods of identifying non-elemental materials based on atomistic properties

Info

Publication number: WO2018098588A1
Application number: PCT/CA2017/051449
Authority: WO
Inventors: Pawel Michal PISARSKI; Scott Richard Holloway
Original assignee: Lumiant Corp
Current assignee: Lumiant Corp
Priority date: 2016-12-02
Filing date: 2017-12-01
Publication date: 2018-06-07
Anticipated expiration: 2019-06-02

Abstract

Disclosed are computer systems for and methods of identifying non-elemental materials based on atomistic properties. The computer systems and methods may permit the identification of non-elemental materials having one or more desired material properties which may be specified in the form of user input. The materials identified may be known or predicted by the computer systems and the methods to exhibit the desired material properties.

Description

TITLE: COMPUTER SYSTEMS FOR AND METHODS OF IDENTIFYING NON-ELEMENTAL MATERIALS BASED ON ATOMISTIC PROPERTIES

CROSS-REFERENCE TO RELATED APPLICATION

[001 ] This application claims priority to United States Patent Application

No. 62/429,529 filed December 2, 2016, the entire contents of which are hereby incorporated herein by reference.

TECHNICAL FIELD

[002] The present disclosure relates to computer systems for and methods of identifying non-elemental materials. More in particular, the present disclosure relates to computer systems and methods involving the use of atomistic models to identify non-elemental materials having certain desired properties.

INTRODUCTION

[003] The following paragraphs are not an admission that anything discussed in them is prior art or part of the knowledge of persons skilled in the art.

[004] There exists a strong demand for novel materials having desirable physical properties. In order to identify a material having a certain desirable physical property, a common approach involves the preparation of a certain quantity of a material thought to possibly exhibit the desirable physical property, and then evaluating the desired physical property by empirical observation and measurement. Thus, for example, material properties, such as melting point, tensile strength, fracture toughness or elasticity, can be measured and quantitated, and based thereon suitability for a particular application of the material can be determined. This approach towards material design in practice frequently yields materials that exhibit one desirable property; however, other material properties can remain suboptimal. Furthermore, advances can rely substantially on the knowledge a material scientist may have of a known material, and based on his or her intuition of the unknown properties of known materials, or more challengingly, the unknown properties of unknown materials.

[005] In this regard, it should be noted that a very large amount of possible non-elemental materials based on the selection of three or more chemical elements from the periodic table is unknown, and that for many known materials based on the selection of two or more elements, a large number of material properties remains unknown. Based on compounds reported in the inorganic crystal structure database (ICSD), it can be estimated that only 6.4% of all possible ternary compounds have been discovered, and 0.15% and 0.003% of all possible quaternary and quintenary compounds, respectively, have been discovered.

[006] Moreover, out of all the possible non-elemental materials that can be synthesized based on the selection of combinations of two elements from the periodic table, only a modest amount of materials has been synthesized in sufficient quantities to empirically evaluate material properties. Thus, the material properties of a substantial amount of non-elemental materials based on combinations of two elements remains unknown. When considering non- elemental materials synthesized based combinations of three or more elements, the preponderance of non-synthesized materials is much larger.

[007] There is therefore a need for new computer systems and methods that permit the identification of non-elemental materials based on one or more desired identified physical properties.

SUMMARY

[008] The following paragraphs are intended to introduce the reader to the more detailed description that follows and not to define or limit the claimed subject matter.

[009] The present disclosure relates to a computer system for the identification of non-elemental materials having defined material property parameters. [0010] In an aspect, the present disclosure provides, in at least one implementation, a computer system for the identification of non-elemental materials having defined material properties, the computer system comprising: a) a first data store assembled in electronic readable format and operable to receive:

material descriptors to uniquely identify a plurality of non- elemental materials;

b) a second data store assembled in electronic readable format and operable to receive:

one or more quantitatively defined values of material property parameters associated with each of the non-elemental materials;

c) a third data store assembled in electronic readable format and operable to receive:

an atomistic fingerprint associated with each of the non- elemental materials;

d) a fourth data store assembled in electronic readable format and operable to receive:

predicted values of material property parameters associated with non-elemental materials for which a quantitative value of a material property parameter is absent in the second data store; and

e) a processing element coupled to the first, second, third, and fourth data stores, and configured to:

access the first, second, third data store to predict the quantitative values of material property parameters associated with the non-elemental materials for which a quantitative value of a material property parameter is absent in second data store; and

access the fourth data store to provide the predicted quantitative values to the fourth data store. [001 1 ] In at least some implementations, the processing element can be configured to predict the quantitative values of the material property parameters by:

(i) identifying a first set of atomistic fingerprints associated with a first set of materials in the first data store wherein for each material included in the first set of materials a quantitative value of a selected material property parameter is defined;

(ii) identifying a second set of one or more atomistic fingerprints associated with a second set of materials in the first data store, wherein the quantitative value of the selected material property parameter associated with the materials included in the second set of materials is absent from the second data store;

(iii) identifying in the first set of atomistic fingerprints one or more distinct fingerprint features that correlate with specific quantitative values of the selected material property parameter; and

(iv) comparing the second set of fingerprints with the first set of fingerprints with respect to the distinct fingerprint features to thereby predict the quantitative values of the selected material property parameter associated with the second set of materials.

[0012] In at least some implementations, the processing element can be configured to perform step (iii) using a machine learning algorithm.

[0013] In at least some implementations, the machine learning algorithm can form a regression model and trains the machine learning algorithm to perform step (iii) to thereby obtain a trained model, and applying the trained model to the second set of fingerprints to perform step (iv).

[0014] In at least some implementations, the material descriptors can include at least one of chemical formulas and crystalline forms.

[0015] In at least some implementations, the computer system can include a user output module coupled to the processing element. [0016] In at least some implementations, the output module can be operable to receive a material property parameter associated with non- elemental materials for which a quantitative value of a material property parameter is absent in the second data store.

[0017] In at least some implementations, the processing element can automatically initiate prediction of the quantitative values upon receipt of a material property parameter associated with non-elemental materials for which a quantitative value of a material property parameter is absent in the second data store.

[0018] In at least some implementations, the computer system includes a user input module coupled to the processing element.

[0019] In at least some implementations, prediction of the quantitative values can be initiated by a user command to the input module.

[0020] In at least some implementations, the user input module can be operable to receive input for at least one of the first, second or third data stores.

[0021 ] In at least some implementations, the computer system can include user input and user output modules, coupled to the processing element, wherein the user input module is operable to receive user input in the form of a quantitatively defined value or range of values for a material property parameter, and the user output is operable to provide one or more non- elemental materials exhibiting the quantitatively defined value or range of values for the material property parameter in the form of user output.

[0022] In at least some implementations, the user output module can separately define the quantitative values of the material property parameters received by the computer system and those predicted by the computer system.

[0023] In another aspect, the present disclosure provides, in at least one implementation, a method of identifying non-elemental materials having defined material property parameters, the method comprising:

a) accessing a first data store comprising material descriptors identifying a plurality of non-elemental materials; b) accessing a second data store comprising one or more quantitatively defined material property parameters associated with each of the non-elemental materials;

c) accessing a third data store comprising an atomistic fingerprint associated with each of the non-elemental materials;

d) predicting the quantitative values of material property parameters associated with the non-elemental materials assembled for which a quantitative value of a material property parameter is absent in second data store; and

e) providing the predicted quantitative values to the fourth data store.

[0024] In at least some implementations, the method can include predicting the quantitative values of the material property parameters by:

[0025] In at least some implementations, the method can include performing step (iii) using a machine learning algorithm. [0026] In at least some implementations, the method can include forming a regression model and training the machine learning algorithm to perform step (iii) to thereby obtain a trained model, and applying the trained model to the second set of fingerprints to perform step (iv).

[0027] In at least some implementations, the method can include receiving the material descriptors in the form of at least one of chemical formulas or crystalline forms.

[0028] In at least some implementations, the method can include receiving a material property parameter associated with non-elemental materials for which a quantitative value of a material property parameter is absent in the second data store by an output module.

[0029] In at least some implementations, the method can include automatically initiating prediction of the quantitative values upon receipt of a material property parameter associated with non-elemental materials for which a quantitative value of a material property parameter is absent in the second data store.

[0030] In at least some implementations, the method can include initiating prediction of the quantitative values by a user command.

[0031 ] In at least some implementations, the method can include receiving input for at least one of the first, second and third data stores.

[0032] In at least some implementations, the method can include receiving user input in the form of a quantitatively defined value or range of values for a material property parameter, and providing one or more non- elemental materials exhibiting the quantitatively defined value or range of values for the material property parameter in the form of user output.

[0033] In at least some implementations, the method can include of claim separately defining the quantitative values of the material property parameters received and those predicted in the form of user output. [0034] In at least some implementations, the method can include empirically testing the predicted quantitative values of the material property parameters to obtain an empirically determined quantitative value of the material property parameter.

[0035] In at least some implementations, the method can include providing the empirically determined quantitative value of the material property parameter in the form of input to the second data store.

[0036] In another aspect, the present disclosure provides, in at least one implementation, a use of a computer system of the present disclosure to identify non-elemental materials exhibiting a defined quantitative value of a material property parameter.

[0037] In another aspect, the present disclosure provides, in at least one implementation, a method of identifying and using a non-elemental material having a defined quantitative material property value, the method comprising:

(a) identifying a non-elemental material having a defined quantitative material property value using a computer system according to the present disclosure;

(b) obtaining the non-elemental material; and

(c) using the non-elemental material in a material application.

[0038] In another aspect, the present disclosure provides, in at least one implementation, a method of identifying and using a non-elemental material having a defined quantitative material property value, the method comprising:

(a) identifying a non-elemental material having a defined quantitative material property value using any method of the present disclosure;

(b) obtaining the non-elemental material; and

(c) using the non-elemental material in a material application.

[0039] In other aspect, the present disclosure provides, in at least one implementation, a computer readable medium comprising a plurality of instructions that, when executed by a processing element, cause the processing element to perform any of the methods of the present disclosure. [0040] Other aspects, features, and implementations of the present disclosure will become apparent to those of ordinary skill in the art upon review of the following description of specific example implementations in conjunction with the drawings. It should be understood, however, that the detailed description, while indicating preferred implementations or the present disclosure, are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those of skill in the art from the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0041 ] The drawings included herewith are for illustrating various examples of apparatuses, systems and methods of the present disclosure and are not intended to limit the scope of what is taught in the disclosure in any way. In the drawings:

[0042] FIGS. 1A, 1 B, 1 C, 1 D, 1 E and 1 F show schematic block diagrams illustrating examples of computer systems;

[0043] FIGS. 2A, 2B, 2C, 2D, 2E, 2F and 2G show schematic flow charts illustrating examples of methods conducted by a computer system;

[0044] FIG. 3 shows a schematic flow chart illustrating an example of a method conducted by a computer system;

[0045] FIG. 4 shows a schematic block diagram illustrating an example of a computer system;

[0046] FIG. 5 shows a schematic flow chart illustrating an example of a method conducted by a computer system;

[0047] FIG. 6 shows a schematic flow chart illustrating another example of a method conducted by a computer system;

[0048] FIG. 7 shows a schematic flow chart of an example method involving the use of a computer system;

[0049] FIG. 8 shows an example of output by a computer system;

[0050] FIG. 9 shows another example of output by a computer system; [0051 ] FIG. 10 shows an example neural network;

[0052] FIGS. 1 1A and 1 1 B show results of training and validation of a computer system using two different material property parameters: density (FIG. 1 1 A) and enthalpy of formation (FIG. 1 1 B);

[0053] FIG. 12 shows results of an example method for generating atomistic fingerprints for titanium aluminide; and

[0054] FIG. 13 shows in example of an input to a computer system.

DETAILED DESCRIPTION

[0055] Various apparatuses, systems or methods will be described below to provide an example of an implementation of each claimed invention. No implementation described below limits any claimed invention and any claimed invention may cover apparatuses, systems and methods that differ from those described below. The claimed inventions are not limited to apparatuses, systems and methods having all of the features of any one apparatus, system or method described below, or to features common to multiple or all of the apparatuses, systems or methods described below. It is possible that an apparatus, system or method described below is not an implementation of any claimed invention. Any invention disclosed in an apparatus, system or method described below that is not claimed in this document may be the subject matter of another protective instrument, for example, a continuing patent application, and the applicant(s), inventor(s) and/or owner(s) do not intend to abandon, disclaim or dedicate to the public any such invention by its disclosure in this document.

Terms and Definitions

[0056] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure pertains. All patents and patent applications, and other publications, cited herein, whether supra or infra, are hereby incorporated by reference herein in their entirety, where permitted. [0057] As used herein, the singular forms "a", "an", and "the" include plural referents unless the content clearly dictates otherwise. Unless otherwise indicated the terms "comprise", "comprises" and "comprising; and "include", "includes" and "including", are used inclusively, rather than exclusively, so that a stated integer or group of integers may include a non-stated integer or group of integers. The term "or" is inclusive unless modified for example by "either".

[0058] It should be noted that terms of degree such as "substantially", "about" and "approximately" as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree should be construed as including a deviation of the modified term if this deviation would not negate the meaning of the term it modifies.

[0059] When ranges are used, for example a range within a certain property parameter, all combinations and sub-combinations or ranges and specific implementations are intended to be included. Other than in the operating examples, or where otherwise indicated, all numerical values or ranges should be understood as being modified by the term "about" when referring to a numerical value or a numerical range, means that the numerical value or numerical range may vary between 1 % and 15% of the stated number or range, as will be readily recognized by the context. Furthermore, any range of values or sub-range within a given range, and all such intermediate values and sub-ranges are individually and specifically disclosed (e.g. , 1 to 5 includes 1 , 1 .5, 2, 2.75, 3. 3.90, 4 and 5).

[0060] The term "computer system", as used herein, refers to any device with an electronic processing element capable of executing instructions, including but not limited to, any supercomputer, mainframe computer, server, personal computer, desktop computer, hand-held computer, lap-top computer, tablet computer, cell phone computer, or smart phone computer or any other suitable electronic device.

[0061 ] The term "coupled" as used herein can have several different meanings depending on the context in which the term is used. For example, the term coupling can have a mechanical or electrical connotation depending on the context in which it is used; i.e. whether describing a physical layout or transmission of data as the case may be. For example, depending on the context, the term coupling may indicate that two elements or devices can be directly physically or electrically connected to one another or connected to one another through one or more intermediate elements or devices via a physical or electrical element such as, but not limited to, a wire, a non-active circuit element (e.g., resistor), wireless connections and the like, for example.

[0062] The term "data store", as used herein, refers to any device or combination of devices capable of storing, accessing, and retrieving data, including, without limitation, any combination and number of data servers, databases, tables, files, lists, queues, directories, data storage devices, data storage media, and the like.

[0063] The terms "element" and "chemical element", as may be used interchangeably herein, refer to chemical elements as set forth in the Periodic Table of Elements, as released and revised from time to time by the International Union of Pure and Applied Chemistry (lUPAC).

[0064] The term "material descriptor", as used herein, refers to any and all information that can be used to describe and thereby identify a non- elemental material.

[0065] The term "output device", as used herein, refers to any device that is used to output information and includes, but is not limited to, one or more of a terminal, a desk top computer, a laptop, a tablet, a cellular phone, a smart phone, a printer (e.g., laser, inkjet, dot matrix), a plotter or other hard copy output device, speaker, headphones, electronic storage device, a radio or other communication device, that could communicate with another device, or any other computing unit. Output devices may include a two dimensional display, such as a TV or a liquid crystal display (LCD), a light-emitting diode (LED) backlit display, a mobile telephone display, a three dimensional display capable of providing output data in a user viewable format. [0066] The term "input device", as used herein, refers to any user operable device that is used to input information and includes but is not limited to, one or more of a terminal, a desk top computer, a laptop, a tablet, a cellular phone, a smart phone, a touch screen, a keyboard, a mouse, a mouse pad, a tracker ball, a joystick, a microphone, a voice recognition system, a light pen, a camera, a data entry device, such as a bar code reader or a magnetic ink character recognition device, sensor or any other computing unit capable of receiving input data. Input devices may include a two dimensional display, such as a TV or a liquid crystal display (LCD), a light-emitting diode (LED) backlit display, a mobile telephone display, a three dimensional display capable of receiving input from a user, e.g., by touch screen. The user in accordance herewith may be any user including any scientist, technician, student or instructor.

General Implementation

[0067] In overview, it has been realized that a computer system can be configured that provides for the rapid identification of non-elemental materials based on one or more user-specified material properties. The memory element of the computer system can be configured to receive and store information relating to non-elemental materials, including material property parameters associated with these materials. The quantitative values of these material property parameters may be either known or unknown. In one implementation, the computer system can predict a previously unknown quantitative value of one or more material property parameters of a non-elemental material. Thus, conveniently, the computer system can provide non-elemental materials exhibiting material properties defined by user input, even if the quantitative values of such material properties were previously unknown. The computer system of the present disclosure can reduce the time needed and costs associated with the discovery of materials having suitable material properties for a certain application of the material, in particular, in instances where it is desired that two, three, four or more desired properties are exhibited by a non- elemental material. [0068] In some implementations, a computer system for the identification of non-elemental materials having defined material properties is provided. The computer system includes a memory element, a processing element and a user input module. The memory element includes four data stores. The first, second and third data store are each capable of receiving and storing certain input information relating to non-elemental materials. The fourth data store is capable of receiving previously unknown quantitative values of material property parameters associated with the non-elemental materials. These previously unknown values can be predicted by the computer system using the information present in the first, second and third data store. The predicted quantitative values of the material property parameters are subsequently available to users wishing to employ the computer system to identify materials with certain desired material properties.

[0069] The first, second and third data stores can receive data via the user input module. The processing element permits and controls communication between the data stores and the input module.

[0070] In some implementations, a computer system is configured to predict the quantitative values of material property parameters of non-elemental materials. In particular, the computer system is configured to predict quantitative values of material property parameters in instances in which these values are absent from the data stores of the computer system. For example, in some methods, a computer system initially receives a series of material descriptors. Each descriptor uniquely identifies certain non-elemental materials. The computer system also receives one or more quantitative values of material property parameters associated with each of the materials. However, for several of the materials no quantitative value is known for a particular material property parameter. Thus, these quantitative values are not received by the computer system and identified as absent therefrom. The method then proceeds to predict the quantitative values. To initiate the prediction, the method first identifies two sets of atomistic fingerprints. The first set of atomistic fingerprints corresponds with materials for which a quantitative value for the material property parameter is present in the data stores of the computer system. The first set of atomistic fingerprints is then used to establish a correlation between certain distinct fingerprint features and specific quantitative values of the material property parameter present in the computer system. This can be achieved, for example, by employing a learning algorithm. The second set of fingerprints is associated with the materials for which the quantitative value of the material property parameter is absent. The quantitative values of the non-elemental material property parameter, which were initially unknown, are then predicted by the computer system by comparing the second set of fingerprints with the first set of fingerprints, with respect to the distinct fingerprint features. The predicted quantitative value is then received by a data store.

[0071 ] An output module can also be included in the computer systems of the present disclosure. For example, a user can through an input module provide one or more desired quantitative values of a material property parameter. An output module can be used to display to a user materials that exhibit the desired quantitative values. In some implementations, the output module can indicate which materials are known to exhibit the desired quantitative values, and which materials have been predicted by the computer system to exhibit the desired quantitative values.

[0072] Selected implementations are described next with reference to the drawings for illustration purposes. It is noted that, initially, details of various selected implementations of computer systems are provided (FIGS. 1A to 1 F), each in conjunction with an accompanying selected implementation of a method to be performed (FIGS. 2A to 2G).

[0073] Referring now to FIG. 1A, the present disclosure provides an example of a computer system 100 including user input module 125, processing element 1 15, memory element 120, and user output module 130. Processing element 1 15 and memory element 120 can be housed together, for example, in terminal 1 10. User input module 125 and user output module 130 are coupled to processing element 1 15 in order to permit communication between processing element 1 15 and user input module 125 and user output module 130.

[0074] Memory element 120 includes a tangible storage medium operable to store computer readable data and instructions. In accordance herewith, the tangible storage medium may be any storage medium, such as, but not limited to, an electrical magnetic or optical storage medium, and the storage medium may be implemented using any suitable technique or methodology known to those skilled in the art, such as, but not limited to random access memory (RAM), disk storage, flash memory, solid state memory, CD-ROM, and so on.

[0075] Memory element 120 further includes first, second, third and fourth data stores 121 , 122, 123, 124, with each data store having data assembled in electronic readable format. The term "assembled in electronic readable format" as used herein means any file, application, module or other data that is useable by processing element 1 15. As used herein, an application or module includes code executable by processing element 1 15 that may be run to carry out one or more functions associated with user input module 125 and the methods to implement the methods of the disclosure. The term "code executable by the processing element" as used herein includes any computer- readable media or commands that may be interpreted by processing element 1 15, such as HTML or XML files, C, C++, SQL or other suitable computer files, that are rendered into user-viewable applications by an application executed by processing element 1 15.

[0076] Processing element 1 15, which is at least one processor or other suitable hardware, is configured to execute and perform computer readable instructions, which may be accessed from a disc, a memory element, or other device capable of storing instructions thereon, and can be arranged in one or more operable modules. These computer readable instructions cause processing element 1 15, upon user operation of the user input module 125, to acquire input data including, quantitatively defined values of material property parameters, and access and process, as herein further described, data from memory element 120 based on the input, and thereafter to transmit processed data to user output module 130 to permit user output module 130 to display output in the form of one more identified non-elemental materials.

[0077] Referring now to FIG. 2A, in conjunction with the example computer system 100, the present disclosure provides an example of a method 200 of identifying non-elemental materials. Method 200 starts with the loading of an interface capable of receiving commands or information in the form of user inputs provided through a user input module (step 210), values or value ranges of material property parameters. After the method 200 is started, it waits for a user input command in the form of a selection of quantitatively defined values or value ranges of material property parameters (step 212).

[0078] By way of example, a user may provide as quantitatively defined input material property parameters: a melting point of at least 600 °C (IP 1 ); a density of 1 mg/ml (IP 2); a melting point between 300 °C and 600 °C, and a density of up to 0.5 mg/ml (IP 3). In accordance herewith, at least one input material property parameter is provided. In some examples, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more input material property parameters or ranges of input material property values are provided.

[0079] The method 200 then screens (step 214) the entries of materials present in the data stores of the computer system for the presence of entries of non-elemental materials having material property parameters of which the quantitative values correspond with those provided by the user (step 210) and displays the materials to a user output module (step 216).

[0080] By way of example, in response to IP 1 , method 200 may provide in the form of output materials M1 , M2 and M3 having a melting points of 700 °C, 900 °C, and 1 , 100 °C, respectively; in response to IP 2, method 200 may provide material M4 and M5, each having a density of 1 mg/ml; and in response to IP 3, the method may provide a material M6 having a melting point between of 350 °C and a density of 0.4 mg/ml. [0081 ] In order to perform the example method 200, data stores 121 , 122, 123 of computer system 100 are initially assembled, as is described hereinafter, with respect to the example implementations of computer systems 101 , 102, 103, 104 as illustrated in FIGS. 1 B to 1 E, and example methods 201 , 202, 203, 204 as illustrated in FIGS. 2B to 2E. In order for the computer system to predict quantitative values of material property parameters and include these predicted values and associated materials in data store 124, at least one quantitative value of a material property parameter associated with a non- elemental material must be absent in the second data store. Upon identification of such absence, a user may initiate the prediction, or the system may automatically initiate prediction of these values. This is further illustrated in FIG. 1 F by example implementation of computer system 105, and example methods 205, 206 in FIG. 2F and FIG. 2G, respectively. It is noted however that, in some implementations, upon the initial assembly of data stores 121 , 122, 123, for example, in such a manner that quantitative values associated with hundreds of non-elemental materials are included therein, the identification by a user of materials with certain desired properties, as exemplified by method 200, and the receipt of further data by data stores 121 , 122, 123, as exemplified by methods 201 , 202, 203, 204, 205, 206, hereinafter described, can proceed more or less contemporaneously. Similarly, the receipt by data store 124 of predicted quantitative values of material properties can proceed more or less contemporaneously with the performance exemplified by method 200.

[0082] Referring now to FIG. 1 B, in accordance with an example computer system 101 , a first data store 121 is configured to receive material descriptors via user input module 125. Each material descriptor is uniquely associated with a non-elemental material and the material descriptors are received in such a form that a unique identification or separate entry of each of the materials in the data store can be achieved. Processing element 1 15 is configured to comprise input module 140 operable to communicate with first data store 121 , and to provide the material descriptors to first data store 121 . First data store 121 is configured to receive and store the material descriptors. [0083] Referring now to FIG. 2B, in conjunction with the example computer system 101 , the present disclosure provides an example method 201. Method 201 starts with the loading of an interface capable of receiving commands or information in the form of user inputs provided through a user input module. The user input consists of material descriptors associated with a plurality of materials, and, as noted, is provided in such a form that the unique identification or separate entry for each of the materials can be achieved (step 220).

[0084] In some implementations, the material descriptors can include a chemical formula for a non-elemental material, for example, a chemical formula or representation based on the Periodic Table of Elements.

[0085] In some implementations, the material descriptors can include information relating to crystalline phases of a non-elemental material, which can, for example, be obtained from the Inorganic Structure Crystal Database (ICSD) (http://www.cds.dl.ac.uk), the crystallography open database (COD) (http://www.crystallograaphy.net), Pauling File (http://www.paulingfile.com) or Pearson's Crystal Data (http://www.crystalimpact.com/pcd/). This can further be expanded upon by computationally derived descriptors, such as electronic band structure, or atomic lattice vibration information, for example computed on the basis of Density Functional Theory (DFT) calculations. Furthermore, in some implementations, the material identifying information can include separate entries for two or more crystalline phases, when a material, although chemically identified by the same formula, is known to exist in multiple crystalline phases. It is noted that the quantitative values for various material property parameters of each of these crystalline phases can vary.

[0086] After method 201 is started, it waits for a user input command in the form of material descriptors (step 222).

[0087] By way of example, a user can provide input in the form of material descriptors relating to non-elemental materials titanium dioxide and aluminum oxide by providing chemical formulas for each of the materials, i.e. Ti0₂ and AI2O3, respectively. The method can then create unique and separate entries in for each of the non-elemental materials.

[0088] By way of further example, several crystalline structures of titanium dioxide are known. Thus, a user can provide a material descriptor by including each of the crystalline forms known as rutile, anatase, and brookite. The method can then create separate unique and separate entries for each of the rutile form, anatase form and brookite form of titanium dioxide.

[0089] Method 201 then provides the material descriptors to a first data store (step 224).

[0090] It will be appreciated to those of skill in the art that in this manner a data store comprising material descriptors for a plurality of materials can be assembled. A data store can include, for example, material descriptors associated with 1 ,000, 10,000, 100,000, 1 ,000,000, 5,000,000 or even more non-elemental materials.

[0091 ] Referring now to FIG. 1 C, in accordance with an example computer system 102, second data store 122 is configured to receive one or more material property parameters associated with the non-elemental materials in the first data store. Processing element 1 15 is configured to comprise input module 150 operable to communicate with second data store 122 and provide the material property parameters to second data store 122. Second data store 122 is configured to receive and store the material property parameters associated with the non-elemental materials.

[0092] Referring now to FIG. 2C, in conjunction with the example computer system 102, the present disclosure provides an example method 202. Method 202 starts with the loading of an interface capable of receiving commands or information in the form of user inputs provided through a user input module. The user input consists of one or more material property parameters associated with the non-elemental materials in the first data store (step 230). After the method 202 is started, it waits for a user input command in the form of one or more material property parameters (step 232). [0093] By way of example, a user can provide a known melting point of 600 °C associated with a material M7, or a user can provide a known melting point of 300 °C, and a known density of 0.7 mg/ml associated with a material M8. Each of these quantitative values associated with materials M7 and M8 can be received by the second data store.

[0094] The method 202 then provides the material property parameters to the second data store (step 234).

[0095] Further examples of material property parameters include: acoustical properties, for example, acoustic absorption; chemical properties, for example, corrosion resistance; electrical properties, for example, dielectric constant; magnetic properties, for example, Curie temperature; manufacturing properties, for example, castability; mechanical properties, for example, brittleness, tensile strength, Young's modulus, hardness, viscosity, fracture toughness and elasticity; optical properties, for example, luminosity; radiological properties, for example, neutron cross section; thermal properties, for example, boiling point, melting point, eutectic point, and vapour pressure.

[0096] Quantitative values for each property parameter for a non- elemental material can be expressed in various units known to those of skill in the art. For example, melting temperature can be expressed in Centigrade (°C) or Degrees Kelvin (K), density can be expressed in milligram per cubic centimeter (mg/cm³) or kilogram per cubic meter (kg/m³), and so forth. In preferred implementations, International Systems of Units (SI Units) are used. Optionally the processing element can include operational modules for conversion of units.

[0097] Known quantitative values of material property parameters are generally values that have been empirically determined using suitable experimental methods or techniques for the determination of these quantitative values. For example, the melting temperature of a solid material can be experimentally determined by heating the material, and measuring the temperature at which the material melts. Known quantitative values of material property parameters can also include quantitative values that have been indirectly determined, for example, theoretical quantitative values deduced from empirical observations. Methods that can be used to obtain theoretical quantitative values of material property parameters include, for example, molecular dynamics based methods or methods based on density functional theory.

[0098] In principle, any and all material property parameters applicable to a non-elemental material are intended to be included herein, and, it should be clear that the present disclosure is not intended to be limited with respect to the actual material property parameters that can be used in accordance herewith.

[0099] In this manner a second data store can be assembled comprising at least one quantitative value of a material property for each material in the first data store. It will be appreciated to those of skill in the art that a data store including more than one quantitative value associated with a plurality of materials can be assembled. The second data store can comprise 5, 10, 15, 20, 25, 50, 100, or more quantitatively defined material properties for one or more materials in the second data store.

[00100] Thus, following the performance of methods 201 and 202, the first data store includes a plurality of material descriptors, each material descriptor uniquely identifying a non-elemental material. The second data store includes for each of these materials at least one quantitatively defined material property parameter.

[00101 ] Attention is drawn to the fact that, in some implementations, not all quantitative values for all non-elemental materials are present in the second data store. To the contrary, for one or materials one or more quantitative values for a material property can be unknown, and therefore absent from the second data store. Thus, by way of example, a material M9 may have been received by the first data store, and known quantitative values for the density of 1 .3 mg/ml and melting temperature of 1 ,700 °C associated with material M9 may have been received by the second data store. At the same time, the boiling temperature of material M9 may be unknown and thus not be received. As herein further detailed, the computer system can be configured to predict the boiling temperature of material M9.

[00102] Referring now to FIG. 1 D, in accordance with an example computer system 103, data store 123 is configured to receive atomistic fingerprints via user input module 125. The atomistic fingerprints in third data store 123 generally corresponds with the material property parameters and associated non-elemental materials in first data store 121 , so that memory element 120 includes for each non-elemental material a material descriptor and at least one material property parameter in first or second data store 121 , 122 and an atomistic fingerprint in third data store 123. The stored atomistic fingerprints can be stored in any kind of format, including, text files, HTML files, images, movies and the like. The atomistic fingerprints can be assembled using any suitable methodology, including, for example, a methodology based on crystal structure, a methodology based on electronic band structure, or a methodology based on vibrations of the crystal lattice.

[00103] Processing element 1 15 is configured to comprise input module 160 operable to provide the atomistic fingerprints to third data store 123, and third data store 123 is configured to receive the atomistic fingerprints. Thus, in this implementation atomistic fingerprints can be entered into a third data store in an assembled fashion. Assembled atomistic fingerprints of non-elemental materials are available for example in: Acta Cryst. 66, 507 (2010); J. Chem. Phys. 130, 104504 (2009); Chem. Phys. Lett. 395, 210 (2004); Phys. Rev. Lett. 108, 058301 (2012); or J. Chem. Theory Comput. 9, 3404 (2013).

[00104] Referring now to FIG. 2D, in conjunction with the example computer system 103, the present disclosure provides an example method 203. Method 203 starts with the loading of an interface capable of receiving commands or information in the form of user inputs provided through a user input module. The user input consists of one or more atomistic fingerprints associated with the non-elemental materials (step 240). After the method 203 is started, it waits for a user input command in the form of one or more atomistic fingerprints associated with the non-elemental materials (step 242). [00105] By way of example, atomistic fingerprints can be provided associated with material M7, M8, and M9. It is noted that, in some implementations, atomistic fingerprints can be provided to the third data store in this manner, for which certain quantitative material property values are absent in the second data store.

[00106] The method 203 then provides the atomistic fingerprints to a third data store (step 244).

[00107] Referring now to FIG. 1 E, in an example computer system 104, the assembly of atomistic fingerprints is performed by processing element 1 15 of the computer system of the present disclosure. Thus, in some implementations, the computer system can include a fifth data store 126 including atomistic fingerprint assembly data, or operable to receive fingerprint assembly data via input module 125. Processing element 1 15 includes atomistic fingerprint processing module 170 to assemble atomistic fingerprints using the fingerprint assembly data. Once assembled, the atomistic fingerprints can be provided to third data store 123 via fingerprint processing module 170. Third data store 123 can be configured to receive the assembled atomistic fingerprints. Atomistic fingerprints can be assembled in the form of numerical matrices on the basis of, for example, crystal structures, electronic band structures, vibrations of the atomic lattice, or a combination thereof. An example assembly of an atomistic fingerprint in the form of a Coulomb matrix is further described in Example 1 . Atomistic fingerprints can also be assembled in the form or vectors, including one-dimensional vectors. Further examples of methods for the assembly of atomistic fingerprints, including in the form of matrices or vectors, are described in: Acta Cryst. 66, 507 (2010); J. Chem. Phys. 130, 104504 (2009); Chem. Phys. Lett. 395, 210 (2004); Phys. Rev. Lett. 108, 058301 (2012); or J. Chem. Theory Comput. 9, 3404 (2013).

[00108] It is noted that one or more atomistic fingerprints can be present in the third data store for a given non-elemental material.

[00109] Referring now to FIG. 2E, in conjunction with the example computer system 104, the present disclosure provides an example method 204. Method 204 starts with the loading of an interface capable of receiving commands or information in the form of user inputs provided through a user input module. The user input consists of one or more atomistic fingerprint assembly data, which can be used to assemble atomistic fingerprints associated with the first and second set of materials (step 250). After method 204 is started, it waits for a user input command in the form of one or more atomistic fingerprint assembly data which can be used to assemble atomistic fingerprints associated with the first and second set of non-elemental materials (step 252). Upon receipt of the atomistic fingerprint assembly data, the data is provided to fifth data store (step 254). The atomistic fingerprint assembly data is then used to assemble atomistic fingerprints (step 256).

[001 10] By way of example, atomistic fingerprint assembly data can be provided to assemble atomistic fingerprints associated with material M7, M8, and M9. It is noted that in some implementations atomistic fingerprint assembly data can be provided to the fifth data store in this manner, for which no quantitative values for material property parameters are present in the second data store.

[001 1 1 ] The method 204 then provides the atomistic fingerprints to the third data store (step 258).

[001 12] To briefly recap, with reference to FIGS. 1 B to 1 E and 2B to 2E, methods can be performed which result in the implementation of a computer system including a memory element comprising a first, second and third data store. The first data store comprises descriptors to uniquely identify a plurality of non-elemental materials. The second data store comprises quantitatively defined material property parameters associated with each of the non- elemental materials. The third data store includes atomistic fingerprints for each of the non-elemental materials. The quantitative values of some material properties associated with the non-elemental materials can be absent from the second data store. These quantitative values of the non-quantitatively defined material properties can be predicted in accordance with some implementations and methods of the present disclosure as will further be described hereinafter with reference to FIGS. 1 F, 2F, 2G and 3.

[001 13] Referring now to FIG. 1 F, in accordance with an example computer system 105, first data store 121 , assembled in electronically readable format, includes one or more material descriptors uniquely associated with a set of non-elemental materials. Second data store 122, assembled in electronically readable format, includes one or more material property parameters associated with the materials, wherein the value of at least one material property parameter is non-quantitatively defined for at least one of the materials. Third data store 123 is further assembled in electronically readable format to include a first set of atomistic fingerprints associated with each of the materials of non-elemental materials. Each of the data stores can be assembled employing user input module 125 and input module 180. Processing element 1 15 is coupled to first, second, third and fourth data stores 121 , 122, 123, 124. Processing element 1 15 is further configured to access first and second data store 121 , 122, and identify material property parameters for which quantitative values are absent in the second data store. Processing element 1 15 is further also configured to access third data store 123 and identify a first set of fingerprints associated with non-elemental materials for which the quantitative values are present in the data store and second set of atomistic fingerprints, associated with non-elemental materials for which the quantitative values of the selected material property parameters are absent from the data store, respectively. Processing element 1 15 is further configured to, upon identification of material property parameters for which quantitative values are absent in the second data store, predict the quantitative value of the absent material property parameters.

[001 14] In some implementations, processing element 1 15 is further configured to identify in the first set of atomistic fingerprints one or more distinct fingerprint features that correlate with the quantitative values of the selected material property parameters. [001 15] In some implementations, processing element 1 15 is further configured to compare the second set of atomistic fingerprints with the first set of atomistic fingerprints with respect to the distinct fingerprint features to thereby predict the quantitative values of the selected material property parameters.

[001 16] Processing element 1 15 is further configured to provide the predicted quantitative value of the material property parameters to fourth data store 124, and fourth data store 124 is configured to receive the predicted quantitative value of the material property parameters.

[001 17] Referring now to FIG. 2F, in conjunction with the example computer system 105, the present disclosure provides an example method 205. The method 205 starts with the loading of an interface capable of receiving commands or information in the form of user inputs provided through a user input module. The user input consists of one or more material property parameters associated with non-elemental materials, wherein the quantitative value of at least one material property parameter is absent from the second data store (step 260). After method 205 is started, it waits for a user input command in the form of non-elemental material descriptors and associated material property parameters, wherein for a material, the quantitative value of at least one material property parameter is absent from the second data store (step 262). Upon identification of the absence of the quantitative value of a material property from the second data store, the prediction of the absent quantitative value can be initiated (step 363).

[001 18] In some implementations, the method includes receiving the identified absence of a quantitative value and providing the identified absence of a quantitative value to an output module. A user may be notified thereof. Thus, for example, the output module may receive a material M10 for which the boiling point is unknown. A user accessing the output module may receive notice on the output module that the computer system has identified that no boiling point is known for material M10. [001 19] In some implementations, the method includes initiating the prediction of the quantitative value via an input module in the form of a user command to initiate the prediction. Thus, by way of example, upon a user having received notice on the output module that no boiling point is known for material M10, a user may opt to initiate the prediction by providing a user command initiating the prediction (step 363).

[00120] The implementations in the foregoing two paragraphs are further illustrated with reference to FIG. 2G. Referring now to FIG. 2G, shown therein is an example method 206. The method starts in a manner similar to method 205 by identifying by identifying the absence of a quantitative value for a material property parameter (step 262). Upon such identification the method provides output to an output module there (step 381 ). The method then waits for an input command (step 382) in the form of an instruction to the method to initiate the prediction (step 363). The method thereafter continues in a manner similar to method 205.

[00121 ] Referring again to FIG. 2F, in some implementations, the method includes an automatic initiation of the prediction of the absent quantitative value upon identification of the absence of the quantitative value of a material property from the second data store. Thus, step 363 can automatically follow step 262.

[00122] Method 206 then identifies a set of materials for which the quantitative values of the material property has been defined (step 364) and creates a first set of non-elemental materials (step 366). Method 206 then identifies a first set atomistic fingerprints associated with the elemental materials in the first set (step 368).

[00123] The first set of atomistic fingerprints then is used to correlate fingerprint features with specific quantitatively defined values of material properties of materials in the first fingerprint set (step 370). In some implementations, this can be achieved by using a machine learning algorithm. In various implementations, this can be achieved by, for example, a support vector regression based learning algorithm, a boosted regression tree based learning algorithm, or a neural network based learning algorithm.

[00124] In some implementations, the machine learning algorithm includes an algorithm capable of forming a regression model, and training the machine learning algorithm. Upon completion of such training, a trained model capable of correlating a finger print feature associated with a non-elemental material to a specific quantitative value of material property parameter is obtained. Thus, by way of example, a certain distinct fingerprint feature present in fingerprints in the first material set may become correlated with a particular quantitative value of a melting temperature of a non-elemental material, or a particular quantitative value for the density of a non-elemental material. Next an example implementation of a training method will be described in further detail.

[00125] Referring now to FIG. 3, shown therein is an example method 300 for the performance of a learning algorithm to train a regression model. The method starts (step 310) by creating pairs 1 -1 connections or references between fingerprints and quantitative material property parameter values (step 315). A set of pairs of fingerprints and quantitative material property parameter values is then assigned for training purposes, and a set of pairs of fingerprints and quantitative material property parameter values is assigned for validation purposes. Generally, pairs of fingerprints and quantitative material property values included in the training set are not included in the validation set and vice versa. The set of training fingerprints and values is then used for machine learning (step 320). Upon completion of training, the validation set of fingerprints and quantitative material property parameter values is applied to the model to test and validate the model (step 325). In the event fingerprints in the validation set are with a sufficient degree of accuracy assigned so that fingerprint features correlate with the quantitative material property parameter values, the method will end (step 340). It is noted that the degree of accuracy can vary and be controlled using a correlation coefficient, for example a Pearson correlation, and relative error of predictions. In the event the model upon application of the validation set fails to correlate fingerprint features with material property parameter values with a sufficient degree of accuracy, the training set can be expanded (step 345). These instances can, for example, be encountered when the set of materials in the database corresponding with the user input parameters is limited, and no meaningful correlation between fingerprint features and quantitative values of material property parameters can be established. Generally during validation, the model is then unable to correlate fingerprint features with quantitative values of material property parameters in the validation set with a sufficient degree of accuracy. However, in the event the model is successfully trained, method 205, 206 can infer quantitative values of material property parameters which are unknown (FIG. 3F and 3G, step 378). Thus, upon completion of method 300, the computer system is configured such that one or more quantitative values of material property parameters which are absent from the computer databases can be predicted. These values thereafter can be stored and used upon query of the computer system by a user to provide a material with desired quantitative values material property parameters. Further examples and details of learning algorithms are provided in Example 1 .

[00126] As will be appreciated by those of skill in the art, other machine learning methods may be selected and employed. A wide variety of machine learning methods is available in this regard, including, for example, the methods documented by I . Goodfellow, Y. Bengio, A. Courville, Deep Learning, Adaptive Computation and Machine Learning, MIT Press, Cambridge 2016; and CM. Bishop, Pattern Recognition and Machine Learning, Information Science and Statistics, Springer 2006.

[00127] Atomistic fingerprint features that may be used to correlate to quantitative values of material property parameters may be any distinct characteristic in a fingerprint, including, for example, any arrangement or pattern of numerical values in a fingerprint assembled in the form of a numerical matrix, which correlates with a quantitative value of a material property parameter. [00128] Referring again now to FIG. 2F, at the same time, or optionally after the performance of steps 364, 366, 368, method 205 identifies the non- elemental materials for which the value of a material property parameter are absent the second data store (step 372) to create a second set of non- elemental materials (step 374). A second set of atomistic fingerprints associated with the second set of non-elemental materials is then identified in the third data store (step 376).

[00129] The second set of atomistic fingerprints is then applied to the trained model, by presenting and applying each atomistic fingerprint in the second set of fingerprints to the trained model. Similarities in fingerprint features between an atomistic fingerprint included in the second set of fingerprints and atomistic fingerprints in the first set of fingerprints are used to predict the unknown quantitative value of a material property parameter of a fingerprint included in the second set of finger prints.

[00130] By way of example, a set of five non-elemental materials (M12- M16) can be received by the data stores where for non-elemental materials M12-M16 the melting point is unknown. The first data store includes 1 ,000 non- elemental materials (M17-M1 ,016) for which the melting point is known. The third data store includes the atomistic fingerprints of materials M17-M1 ,016, as well as the atomistic fingerprints of M12-M16. A machine learning algorithm is formed and trained to correlate melting temperatures to fingerprint features of materials M17-M 1 ,016. The method then proceeds to predict the melting temperature of the non-elemental materials M12-M16 by applying the trained regression model to fingerprints of M12-M 16. In this manner, the method may provide, for example, a predicted melting temperature of M12 of 1 ,000 °C, M13 of 200 °C, M 14 of 350 °C, M 15 of 390 °C and M 16 of 1 ,200 °C.

[00131 ] The predicted quantitative value of a material parameter is provided to the fourth data store, and received by the fourth data store (step 379). In this manner, the amount of data present in the computer system can be increased each time the method exemplified by method 205 is performed. It will be clear to those of skill in the art that the accuracy with which the computer system of the present disclosure can predict quantitative values can increase as the quantity of material property parameters and the associated materials in the data stores increases.

[00132] As previously noted, in some implementations, for example as illustrated by method 200, user output is provided to a user output module in the form of materials that exhibit certain user specified material characteristics. In some implementations, the user output is provided in a manner in which the user can identify whether the material characteristics are known, or predicted by the computer system.

[00133] In some implementations, the predicted quantitative values of a material property parameter can be tested experimentally. By way of example, if a material M1017 is presented as having a predicted melting temperature of 300 °C, a quantity of the material may be obtained and the melting temperature may be determined experimentally. Upon experimental confirmation of a predicted quantitative value for a material property parameter, such value may be entered into the second data store, and output may thereafter no longer be presented as a predicted quantitative value.

[00134] Referring now to FIG. 6, shown therein is an example method 600, for determining the quantitative value of a material property parameter following prediction by a computer system of the present disclosure. The method starts with providing an input value or range of values of material property parameters (step 605). The computer system then predicts one or more quantitative values (step 610) and provides output (step 615) in the form of non-elemental materials exhibiting quantitative values of material property parameters corresponding with the input. It is noted that steps 605, 610 and 615 together correspond with example method 205. The non-elemental materials are then synthesized or otherwise obtained (step 620) and tested experimentally (step 625), typically in a laboratory, in order to empirically determine the quantitative value of the material property parameter (step 630). The empirically determined quantitative value of the material property parameter can be compared with the predicted value, and the empirically determined quantitative value is provided as input to the computer system (step 635). It is noted that step 635 corresponds with example method 202. The computer system may be configured to retain both the predicted value and the empirical value, or in other implementations, the predicted value may be removed from the data store upon the addition of an experimental value.

[00135] It will now be apparent that the computer system of the present disclosure can be configured to perform a plurality of methods as exemplified by methods 200, 201 , 202, 203, 204, 205, 206. Referring now to FIG. 5, these exemplified methods can be summarized in example method 500. Example method 500 starts with the loading of an interface capable of receiving commands or information in the form of user inputs provided through a user input module. User input can consist of: (i) one or more desired quantitative values of material property parameters (step 502); (ii) material descriptors associated with a non-elemental material (step 512); (iii) quantitatively defined values for material property parameters associated with a material (step 522); (iv) atomistic fingerprints, or data for the assembly thereof associated with a material (step 532). In some implementations, the computer system can identify that a non-quantitatively defined value for material property parameters associated with a material is absent in the computer system (step 542). After method 500 is started, it waits for a user input command in the form of any one of the aforementioned (i), (ii), (iii) or (iv). Then, method 500 proceeds depending on the input provided. If input (i) is provided, method 500 will identify materials corresponding with the desired quantitative values of material property parameters (step 504) and display the materials to an output module (step 506). If input (ii) is provided, method 500 proceeds with providing the material descriptors to a first data store (step 514). If input (iii) is provided, method 500 proceeds with providing the quantitatively defined values for material property parameters associated with the material to a second data store (step 524). If input (iv) is provided then method 500 proceeds with providing atomistic fingerprints, or data for the assembly thereof associated with the material to a third data store (step 534). If the computer identifies that a quantitative value for a material property parameter associated with a material is absent, is then method 500 proceeds with prediction of these quantitative values (step 544), and inclusion of the predicted material property parameters to the fourth data store (step 546). It is noted that the method relating to the provision of input (i) corresponds with the exemplified method 200, hereinbefore described; the method relating to the provision of input (ii) corresponds with exemplified method 201 , hereinbefore described; the method corresponding with the provision of input (iii) corresponds with exemplified method 202, hereinbefore described; and the method relating to the provision of input (iv) corresponds with the exemplified methods 203, 204, hereinbefore described. The method relating to the prediction of quantitative values corresponds with exemplified methods 205, 206, hereinbefore described.

[00136] In some implementations of the present disclosure, a computer system can be configured to include a plurality of user input modules, or a plurality of user output modules. Referring now to FIG. 4, the present disclosure provides, in an implementation, an example of a computer system 400 including user input module 125a, 125b, 125c, processing element 1 15, memory element 120, and user output modules 130a, 130b and 130c. Processing element 1 15 and memory element 120 can be housed together, for example, in terminal 1 10. User input modules 125a, 125b and 125c and user output modules 130a, 130b, and 130c are coupled to processing element 1 15 via network 410 and 420, respectively, in order to permit communication between processing element 1 15 and user input modules 125a, 125b and 125c, and user output module 130a, 130b, and 130c, respectively. In different implementations, user input modules 125a, 125b, and 125c can be configured so that one or more of the methods described herein, and, as hereinbefore exemplified, by method 200, 201 , 202, 203, 204, 205 or 206 can be performed. Thus, for example, a user input module (e.g., user input module 125a) can be configured so that it can only perform an implementation of the method exemplified by method 200; and simultaneously a user input module (e.g., 125b) can be configured so that it can only perform the method exemplified by method 201 . In this manner, a variety of configurations involving a plurality of user input modules or user output modules can be assembled. It will be clear to those of skill in the art that, depending on the specific configuration selected, some of the herein described parts can become superfluous and can be omitted in such configuration. Thus, for example, in configurations including only input modules for the performance of the method exemplified by method 200, the processing element does not need to be configured to be operable to access the third data store.

[00137] Turning now further to uses of the computer systems and methods of the present disclosure, as will now further be apparent, the computer system and methods of the present disclosure permit the identification by a user of non-elemental materials having one or more specified desired material property parameter values. Upon identification, the non- elemental materials may be obtained and used in a material application, notably advantageously, in an application in which it is of significance that the material exhibits the desired quantitative values of the material property parameters. It will be understood by those of skill in the art that material applications can range across an extremely wide spectrum, including industrial or domestic applications, depending on the desired function and performance of the material. Material applications include, without limitation, engineering applications, such as mechanical, chemical and civil engineering applications; electronic applications; metallurgical applications; and medical applications. It is noted that, depending on the number of entries in the data stores of the computer system, and further depending on the specified desired quantitative values, more or less materials exhibiting the desired quantitative values of the material property parameters may be identified by the computer system. It is further noted that by using the computer system of the present disclosure non- elemental materials may be identified and used in material applications in which the materials have never previously been used. Thus, it will be clear that the computer system and methods of the present disclosure can be employed to facilitate the discovery of novel material applications.

[00138] Accordingly, in one aspect, the present disclosure includes a method of identifying and using a non-elemental material having a defined quantitative material property value in a material application. In accordance herewith the method can include the steps of (a) identifying a non-elemental material having a defined quantitative material property value using a computer system or method according to the present disclosure; (b) obtaining the non- elemental material; and (c) using the non-elemental material in a material application. This method is now further illustrated with reference to FIG. 7.

[00139] Referring now to FIG. 7, shown therein is an example method 700, for the identification and application of a non-elemental material having a defined quantitative material property value. Method 700 starts with the identification of desired material properties for an application (step 705). A desired quantitative value or range of values of one or more material property parameters is then provided in the form of input to the computer system (step 710). The computer system then identifies materials exhibiting the desired values of material property parameters, which as will be clear from the foregoing, can be either previously experimentally determined or predicted by the computer system (step 715), and provides non-elemental materials in the form of output (step 720). The material can then be obtained (step 725), for example by synthesizing the material, and tested and used in the material application (step 730).

[00140] At least some of the elements of the various computer systems described herein are implemented via software and may be written in a high- level procedural language such as object oriented programming or a scripting language. Accordingly, the program code may be written in C, C++ or any other suitable programming language and may include modules or classes, as is known to those skilled in object oriented programming. Alternatively, at least some of the elements of the various computer systems described herein that are implemented via software may be written in assembly language, machine language or firmware. In either case, the program code can be stored on a storage media or on a computer readable medium that is readable by a general or special purpose electronic device having a processor, an operating system and the associated hardware and software that implements the functionality of at least one of the implementations described herein. The program code, when read by the electronic device, configures the electronic device to operate in a new, specific and defined manner in order to perform at least one of the methods described herein.

[00141 ] Furthermore, at least some of the methods described herein are capable of being distributed in a computer program product including a transitory or non-transitory computer readable medium that bears computer usable instructions for one or more processors. The medium may be provided in various forms such as, but not limited to, one or more diskettes, compact disks, tapes, chips, USB keys, external hard drives, wire-line transmissions, satellite transmissions, internet transmissions or downloads, magnetic and electronic storage media, digital and analog signals, tablet (e.g., iPad) or smartphone (e.g., iPhones) apps, and the like. The computer useable instructions may also be in various forms, including compiled and non-compiled code.

[00142] Hereinafter is provided at least one example of the present disclosure. The at least one example is provided for the purpose of illustration only and is not intended to limit the scope of the present disclosure in any way. Furthermore, it will be appreciated that the scope of the claims should not be limited by the preferred implementations set forth in the description, but should be given the broadest interpretation consistent with the description as a whole.

EXAMPLE 1

Implementation of a computer system for rapid material discovery

[00143] An example computational system was assembled to include an input and output module, as well as a processing element and access to data stores, as shown in FIG. 1 . In order to start a material discovery process, a user can provide a query to the input module of the computer system. In particular, a user can specify a desired property, or set of material properties, as well as a value or range of values for each one of the material properties he/she wishes to investigate. The output module displays materials exhibiting the specified quantitative values of the material property or properties provided by the user.

[00144] It is an advantage of the herein described example computer system and methodology, that the computer system and methodology allow the identification of materials defined not only by a single material property, but by two or more properties, of which the quantitative values can be either empirically or theoretically determined, or predicted by means of a machine learning algorithm incorporated in the system.

[00145] Identifying a material that will satisfy a particular technical and /or market need can be challenging. The more specific requirements one wishes for the material property to meet, the harder it can be to meet all such requirements in a single material. It should be noted that material properties generally come with some tradeoffs and the more constraints one specifies in a search the more difficult it is to find a material that meets all of the desired constraints.

[00146] To enable a user driven search, the computer system includes data stores which include a set of quantitative values of material properties associated with a wide range of materials. Values for material property parameters, which are empirically known, or theoretically derived, have been stored in a second data store, and material property values as predicted by machine learning algorithm have been stored in a separate data store namely a fourth data store (see FIG. 1 ). A first data store holds information to uniquely identify each material present in the computer system. These data stores, specifically the second and fourth data store, are searched for materials in response to a user query. Materials corresponding with the desired quantitative property values provided are displayed to the user in the form of results on the output module.

[00147] As noted, the fourth data store is populated with predicted values of material parameters. Predictions are made by the computer system on the basis of atomistic fingerprints of each of the materials in first data store by a machine learning algorithm. In the present example a neural network algorithm is further described. However, one can also use boosted regression trees, or support vector regression, or any other machine learning algorithms.

[00148] The inventors have built training and validation sets for a neural network algorithm from a dataset comprising entries from the second and third data stores entries as described below. Once the neural network has been trained and the accuracy of the results were evaluated, based on a validation set, the inventors used it to predict the same set of properties for other compound for which fingerprints have been identified and stored in the third data store. The database comprising of first, second, third and fourth data stores, is an integral part of a broader computing platform.

Data Stores

[00149] The data and information contained within the computing platform includes:

Information about the materials, their chemical composition, stoichiometry and thermodynamic phase as defined by crystal structure of compounds. The data can originate from various sources like for example ICSD, COD, Pauling File, Pearson's Crystal Data. This data can be further augmented with computationally derived descriptors like electronic band structure, or atomic lattice vibration information computed on the basis of Density Functional Theory (DFT) calculations (included in the first data store).

Information about empirically measured values of material properties. This data can originate from various sources (included in the second data store).

Information about theoretically derived values of material properties, for example on the basis of DFT, or Molecular Dynamics, or any other computational method enabling theoretical prediction of material properties (included in the second data store). Information about compounds' fingerprints - map like characteristics of materials derived on the basis of crystal structures and/or the DFT calculations (included in the third data store).

Atomistic Fingerprints

[00150] Fingerprints can be based on many types of data and contain variety of information about crystal structure, electronic band structure, vibrations of the atomic lattice, or even the combination of these groups. Their purpose is to build features for the machine learning algorithms.

[00151 ] Features for a machine learning algorithm can be constructed in many ways. For example, by using methods of constructing fingerprints based on atomic coordinates and types. Methods include the methods describe in Phys. Rev. Lett. 108, 058301 (2012), which is believed to provide complete description for molecular systems.

[00152] Intermolecular Coulomb repulsion operators, also referred to as Coulomb matrices, are given by

where Z_t are atomic numbers, R_t are atomic coordinates, and indexes i,j iterate atoms of given crystal structure. The off-diagonal terms describe ionic repulsion between atoms i and j, whereas the diagonal terms are obtained from a fit of atomic numbers to the energies of isolated atoms (Phys. Rev. Lett. 108, 058301 (2012)). It should be noted that Coulomb matrices contain a full set of information that DFT calculations take as inputs (Z_t and ), however, the description they provide is not unique. Any given system of atoms can be represented by more than one matrix by simply reshuffling the indexes of the atoms. Several methods to overcome this problem have been proposed as described in (J. Comp. Theory Comput. 9, 3404 (2013)).

[00153] To overcome the problem of non-unique Coulomb matrices, the inventors have developed a method which generates and uses multiple fingerprints per compound and after completing materials property predictions based on individual fingerprints it averages the results.

[00154] First, a single atom is selected and based on individual positions of neighboring atoms Euclidian norms are constructed and used to radially sort all atoms in the crystal. Having an ascending list of Euclidian distances between neighboring atoms, the number of unit cell repetitions needed is calculated to generate the Coulomb matrices of a previously specified size. In this example, matrices of size 20x20 have been used. This is the size of the fingerprint matrices. An example fingerprint of titanium aluminide is shown in FIG. 12.

Learning Algorithm

[00155] A neural network algorithm mimics a biological network of neurons interconnected together to form a structure. Many simulated neurons, which in the algorithm can be considered as signal processors, are connected and analogously form an artificial neural network structure. Each neuron receives input signals and transmits a response when the total input exceeds threshold value. One can think about this signal processor as a neuron with a Heaviside activation function. Neural networks are one of the most popular methods used to solve classification problems, but they can also be applied in regression (T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning, (Springer series in statistics Springer 2008).

[00156] A neural network takes feature vectors as input and using multiple layers of neurons the algorithm creates nonlinear features which can be further used for regression. The neurons are organized in layers for which output of one layer becomes an input for next layer. Only adjacent layers are connected together through weights and activation functions and the parameters are obtained by optimizing the loss function using training data. An example structure of a single layer neural network is shown in FIG. 10. [00157] The action of each layer of neurons given an input vector of features ⁽¹⁾ = (l, x₁, x₂, x₃, ... , x_u ... , x_p) is expressed algebraically by the following equations:

_Ω(2) ₌ . _Ω(1)₎ y = 0⁽² · a<²>

(2)

When constructing the input feature vector one needs to add 1 as the bias feature to account for the constant term in regression. Next, in the hidden layer non-linear activation function g(x) is used and a transformation parametrized by coefficients derived feature vector ⁽²⁾ is obtained from feature vector (¹⁾. The output layer serves as the prediction vector.

[00158] There are several choices for the activation function. In this example, a frequently used function given by equation (3) is used:

g(e - x) = tanh(0^■ x) (3)

To obtain more complex nonlinear relationships between features x and the output layer more hidden layers can be included. The elements of all weight matrices Θ as the model parameters, can be determined during training (learning) process. The number of layers and number of neurons in the layer define the network architecture.

[00159] Example results shown in FIGS. 1 1A and 1 1 B were obtained using a neural network of 400-600-200-40-1 nodes respectively. The first layer was constructed to accept the fingerprints. The elements of 20x20 matrices as shown in FIG. 10, were rearranged to form a vector. To accomplish this, the matrix was rewritten so that all columns of the fingerprint matrix were assembled together to form a 400 entries long vector. Nonlinear activation function was applied to the next two hidden layers, as defined by equation (3). To the last two layers linear activation functions were applied.

[00160] To perform this example 24,252 fingerprint - material property value pairs in the training data set were used and 2,695 data points in the validation data set were used. FIG. 1 1A shows results obtained for mass density for both training and validation data sets. FIG. 1 1 B shows results for formation enthalpy.

[00161 ] The material property values and fingerprints themselves were stored in the second and third data store, respectively.

EXAMPLE 2

Implementation of a computer system for rapid material discovery - example query

[00162] An example computer system was assembled and provided with an example user input to identify materials having a density of <3 g/cm³ The computer system provided the following materials: distrontium chromium antimonate, silico, tetrasodium dimanganese (II I) oxide, calcium magnesium bis(cyclo-trioxopentahydroxotriborate) hexahydrate, potassium cerium (I II) cerium (IV) tetrakis(sulfate(VI)), as shown in FIG. 8. It is noted that some materials are predicted (see: under column labeled "Type") to exhibit a density of <3 g/cm³ (e.g., distrontium chromium antimonate), while for another material the density of <3 g/cm³ has been experimentally determined (Silicon) (see: under column labeled "Type").

EXAMPLE 3

[00163] An example computer system was assembled and provided with an example user input to identify materials having a density of <3 g/cm³ and an enthalpy of formation of <-200 kJ/mol. The computer system provided the following materials: potassium sodium iron cyclo-tetrasilicate; lanthanum gold indium, neodymium copper oxide, zinc-dilaluminum sulfide (alpha) and ammonium tetrachloroplatinate (II), as shown in FIG. 9. It is noted that some materials are predicted (see: under column labeled "Type") to exhibit a density of <3 g/cm³ and an enthalpy of formation of <-200 kJ/mol (e.g. , potassium sodium iron cyclo-tetrasilicate), while for another material the density of <3 g/cm³ an enthalpy of formation of <-200 kJ/mol has been experimentally determined (ammonium tetrachloroplatinate (II)) (see: under column labeled "Type").

EXAMPLE 4

Implementation of a computer system for rapid material discovery - data store input

[00164] An example computer system was assembled and provided with an example input for inclusion in the data store. As shown in FIG. 13, two quantitative values of two material property parameters (density and enthalpy of formation) for two materials (cobalt antimonide and copper phosphide) were provided to the computer system. Upon providing the input, the data store of the computer comprised a value of 7.61 g/cm³ and -62.761 kJ/mol for the density and enthalpy of formation, respectively, for cobalt antimonide, and a value of 4310 kg/m³ and -121.001 kJ/mol, respectively, for the density and enthalpy of formation, respectively, for copper phosphide.

Claims

CLAIMS We claim:

1 . A computer system for the identification of non-elemental materials having defined material properties, the computer system comprising:

a) a first data store assembled in electronic readable format and operable to receive:

an atomistic fingerprint associated with each of the non- elemental materials;

access the first, second, third data store to predict the quantitative values of material property parameters associated with the non-elemental materials for which a quantitative value of a material property parameter is absent in second data store; and access the fourth data store to provide the predicted quantitative values to the fourth data store.

2. The computer system of claim 1 , wherein the processing element is configured to predict the quantitative values of the material property parameters by:

3. The computer system of claim 2, wherein the processing element is configured to perform step (iii) using a machine learning algorithm.

4. The computer system of claim 3, wherein the machine learning algorithm forms a regression model and trains the machine learning algorithm to perform step (iii) to thereby obtain a trained model, and applying the trained model to the second set of fingerprints to perform step (iv).

5. The computer system of any one of claims 1 to 4, wherein the material descriptors comprise at least one of chemical formulas and crystalline forms.

6. The computer system of any one of claims 1 to 5, comprising a user output module coupled to the processing element.

7. The computer system of claim 6, wherein the output module is operable to receive a material property parameter associated with non-elemental materials for which a quantitative value of a material property parameter is absent in the second data store.

8. The computer system of claim 1 , wherein the processing element automatically initiates prediction of the quantitative values upon receipt of a material property parameter associated with non-elemental materials for which a quantitative value of a material property parameter is absent in the second data store.

9. The computer system of any one of claims 1 to 8, comprising a user input module coupled to the processing element.

10. The computer system of claim 9, wherein prediction of the quantitative values is initiated by a user command to the input module.

1 1 . The computer system of claim 9 or 10, wherein the user input module is operable to receive input for at least one of the first, second and third data stores.

12. The computer system of any one of claims 1 to 1 1 , comprising user input and user output modules, coupled to the processing element, wherein the user input module is operable to receive user input in the form of a quantitatively defined value or range of values for a material property parameter, and the user output is operable to provide one or more non-elemental materials exhibiting the quantitatively defined value or range of values for the material property parameter in the form of user output.

13. The computer system of claim 1 , wherein the user output module separately defines the quantitative values of the material property parameters received by the computer system and those predicted by the computer system.

14. A method of identifying non-elemental materials having defined material property parameters, the method comprising: a) accessing a first data store comprising material descriptors identifying a plurality of non-elemental materials;

b) accessing a second data store comprising one or more quantitatively defined material property parameters associated with each of the non-elemental materials;

d) predicting the quantitative values of material property parameters associated with the non-elemental materials assembled for which a quantitative value of a material property parameter is absent in the second data store; and e) providing the predicted quantitative values to the fourth data store.

15. The method of claim 14, comprising predicting the quantitative values of the material property parameters by:

16. The method of claim 15, comprising performing step (iii) using a machine learning algorithm.

17. The method of claim 16, comprising forming a regression model and training the machine learning algorithm to perform step (iii) to thereby obtain a trained model, and applying the trained model to the second set of fingerprints to perform step (iv).

18. The method of any one of claims 13 to 17, comprising receiving the material descriptors in the form of at least one of chemical formulas and crystalline forms.

19. The method of any one of claims 13 to 18, comprising receiving a material property parameter associated with non-elemental materials for which a quantitative value of a material property parameter is absent in the second data store.

20. The method of claim 19, comprising automatically initiating prediction of the quantitative values upon receipt of a material property parameter associated with non-elemental materials for which a quantitative value of a material property parameter is absent in the second data store.

21 . The method of any one of claims 14 to 20, comprising initiating prediction of the quantitative values by a user command.

22. The method of any one of claims 14 to 21 , comprising receiving input for at least one of the first, second and third data stores.

23. The method of any one of claims 14 to 22, comprising receiving user input in the form of a quantitatively defined value or range of values for a material property parameter, and providing one or more non-elemental materials exhibiting the quantitatively defined value or range of values for the material property parameter in the form of user output.

24. The method of claim 23, wherein the user output separately defines the quantitative values of the material property parameters received and those predicted.

25. The method of claim 23 or 24, wherein the method further comprises empirically testing the predicted quantitative values of the material property parameters to obtain an empirically determined quantitative value of the material property parameter.

26. The method of claim 25, wherein the method further comprises providing the empirically determined quantitative value of the material property parameter in the form of input to the second data store.

27. A use of the computer system of any one of claims 1 to 13 to identify non-elemental materials exhibiting a defined quantitative value of a material property parameter.

28. A computer readable medium comprising a plurality of instructions that, when executed by a processing element, cause the processing element to perform the method of any one of claims 14 to 26.

29. A method of identifying and using a non-elemental material having a defined quantitative material property value, the method comprising:

identifying a non-elemental material having a defined quantitative material property value using a computer system of any one of claims 1 to 13; obtaining the non-elemental material; and

using the non-elemental material in a material application.

30. A method of identifying and using a non-elemental material having a defined quantitative material property value, the method comprising:

identifying a non-elemental material having a defined quantitative material property value using a method of any one of claims 14 to 26;

obtaining the non-elemental material; and

using the non-elemental material in a material application.

31 . A system and/or method comprising any combination of one or more of the features described above and/or claimed above and/or illustrated in the drawings.