| Machine learning in materials informatics: recent applications and prospects R Ramprasad, R Batra, G Pilania, A Mannodi-Kanakkithodi, C Kim npj Computational Materials 3 (1), 54, 2017 | 1713 | 2017 |
| Accelerating materials property predictions using machine learning G Pilania, C Wang, X Jiang, S Rajasekaran, R Ramprasad Scientific reports 3 (1), 2810, 2013 | 964 | 2013 |
| Machine learning bandgaps of double perovskites G Pilania, A Mannodi-Kanakkithodi, BP Uberuaga, R Ramprasad, ... Scientific reports 6 (1), 19375, 2016 | 546 | 2016 |
| The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design K Choudhary, KF Garrity, ACE Reid, B DeCost, AJ Biacchi, ... npj computational materials 6 (1), 173, 2020 | 522 | 2020 |
| Machine Learning Strategy for Accelerated Design of Polymer Dielectrics A Mannodi-Kanakkithodi, G Pilania, TD Huan, T Lookman, R Ramprasad Scientific Reports 6, 20952, 2016 | 417 | 2016 |
| Rational design of all organic polymer dielectrics V Sharma, C Wang, RG Lorenzini, R Ma, Q Zhu, DW Sinkovits, G Pilania, ... Nature communications 5 (1), 4845, 2014 | 362 | 2014 |
| Multi-fidelity machine learning models for accurate bandgap predictions of solids G Pilania, JE Gubernatis, T Lookman Computational Materials Science 129, 156-163, 2017 | 350 | 2017 |
| Polymer informatics: Current status and critical next steps L Chen, G Pilania, R Batra, TD Huan, C Kim, C Kuenneth, R Ramprasad Materials Science and Engineering: R: Reports 144, 100595, 2021 | 291 | 2021 |
| Machine Learning in Materials Science: From Explainable Predictions to Autonomous Design G Pilania Computational Materials Science 193, 110360, 2021 | 282 | 2021 |
| From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown CK Kim, G Pilania, R Ramprasad Chemistry of Materials, 2016 | 255 | 2016 |
| A polymer dataset for accelerated property prediction and design TD Huan, A Mannodi-Kanakkithodi, C Kim, V Sharma, G Pilania, ... Scientific Data 3, 160012, 2016 | 234 | 2016 |
| Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites C Kim, G Pilania, R Ramprasad The Journal of Physical Chemistry C 120 (27), 14575-14580, 2016 | 230 | 2016 |
| Finding new perovskite halides via machine learning G Pilania, PV Balachandran, C Kim, T Lookman Frontiers in Materials 3, 19, 2016 | 227 | 2016 |
| Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond A Mannodi-Kanakkithodi, A Chandrasekaran, C Kim, TD Huan, G Pilania, ... Materials Today, 2018 | 221 | 2018 |
| Machine-learning-based predictive modeling of glass transition temperatures: a case of polyhydroxyalkanoate homopolymers and copolymers G Pilania, CN Iverson, T Lookman, BL Marrone Journal of Chemical Information and Modeling 59 (12), 5013-5025, 2019 | 169 | 2019 |
| Computational strategies for polymer dielectrics design CC Wang, G Pilania, SA Boggs, S Kumar, C Breneman, R Ramprasad Polymer 55 (4), 979-988, 2014 | 160 | 2014 |
| Design of functional and sustainable polymers assisted by artificial intelligence H Tran, R Gurnani, C Kim, G Pilania, HK Kwon, RP Lively, R Ramprasad Nature Reviews Materials 9 (12), 866-886, 2024 | 153 | 2024 |
| A machine learning approach for the prediction of formability and thermodynamic stability of single and double perovskite oxides A Talapatra, BP Uberuaga, CR Stanek, G Pilania Chemistry of materials 33 (3), 845-858, 2021 | 134 | 2021 |
| Machine learning in nuclear materials research D Morgan, G Pilania, A Couet, BP Uberuaga, C Sun, J Li Current Opinion in Solid State and Materials Science 26 (2), 100975, 2022 | 129 | 2022 |
| Machine learning for materials design and discovery R Vasudevan, G Pilania, PV Balachandran Journal of Applied Physics 129 (7), 2021 | 124 | 2021 |
Rampi RamprasadGeorgia Institute of TechnologyVerified email at gatech.edu
