| Understanding the diversity of the metal-organic framework ecosystem SM Moosavi, A Nandy, KM Jablonka, D Ongari, JP Janet, PG Boyd, Y Lee, ... Nature Communications 11 (1), 1-10, 2020 | 653 | 2020 |
| Big-Data Science in Porous Materials: Materials Genomics and Machine Learning KM Jablonka, D Ongari, SM Moosavi, B Smit Chemical Reviews, 2020 | 588 | 2020 |
| Leveraging large language models for predictive chemistry KM Jablonka, P Schwaller, A Ortega-Guerrero, B Smit Nature Machine Intelligence 6 (2), 161-169, 2024 | 494* | 2024 |
| The Role of Machine Learning in the Understanding and Design of Materials SM Moosavi, KM Jablonka, B Smit Journal of the American Chemical Society 142 (48), 20273-20287, 2020 | 437 | 2020 |
| SELFIES and the future of molecular string representations M Krenn, Q Ai, S Barthel, N Carson, A Frei, NC Frey, P Friederich, ... Patterns 3 (10), 2022 | 254 | 2022 |
| 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon KM Jablonka, Q Ai, A Al-Feghali, S Badhwar, JD Bocarsly, AM Bran, ... Digital Discovery 2 (5), 1233-1250, 2023 | 224 | 2023 |
| Bias free multiobjective active learning for materials design and discovery KM Jablonka, GM Jothiappan, S Wang, B Smit, B Yoo Nature communications 12 (1), 2312, 2021 | 214 | 2021 |
| A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists A Mirza, N Alampara, S Kunchapu, M Ríos-García, B Emoekabu, ... Nature Chemistry, 1-8, 2025 | 134* | 2025 |
| Diversifying databases of metal organic frameworks for high-throughput computational screening S Majumdar, SM Moosavi, KM Jablonka, D Ongari, B Smit ACS applied materials & interfaces 13 (51), 61004-61014, 2021 | 128 | 2021 |
| From text to insight: large language models for chemical data extraction M Schilling-Wilhelmi, M Ríos-García, S Shabih, MV Gil, S Miret, CT Koch, ... Chemical Society Reviews, 2025 | 124 | 2025 |
| Charge Separation and Charge Carrier Mobility in Photocatalytic Metal‐Organic Frameworks M Fumanal, A Ortega‐Guerrero, KM Jablonka, B Smit, I Tavernelli Advanced Functional Materials 30 (49), 2003792, 2020 | 97 | 2020 |
| Making the collective knowledge of chemistry open and machine actionable KM Jablonka, L Patiny, B Smit Nature Chemistry 14 (4), 365-376, 2022 | 94 | 2022 |
| A holistic platform for accelerating sorbent-based carbon capture C Charalambous, E Moubarak, J Schilling, E Sanchez Fernandez, ... Nature 632 (8023), 89-94, 2024 | 88* | 2024 |
| Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks KM Jablonka, D Ongari, SM Moosavi, B Smit Nature Chemistry 13 (8), 771-777, 2021 | 84 | 2021 |
| An ecosystem for digital reticular chemistry KM Jablonka, AS Rosen, AS Krishnapriyan, B Smit ACS Central Science 9 (4), 563-581, 2023 | 54 | 2023 |
| Applicability of Tail Corrections in the Molecular Simulations of Porous Materials KM Jablonka, D Ongari, B Smit Journal of chemical theory and computation 15 (10), 5635-5641, 2019 | 52 | 2019 |
| Assessment of fine-tuned large language models for real-world chemistry and material science applications J Van Herck, MV Gil, KM Jablonka, A Abrudan, AS Anker, M Asgari, ... Chemical Science 16 (2), 670-684, 2025 | 50 | 2025 |
| Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant KM Jablonka, C Charalambous, E Sanchez Fernandez, G Wiechers, ... Science Advances 9 (1), eadc9576, 2023 | 49 | 2023 |
| Probing the limitations of multimodal language models for chemistry and materials research N Alampara, M Schilling-Wilhelmi, M Ríos-García, I Mandal, P Khetarpal, ... Nature computational science 5 (10), 952-961, 2025 | 41* | 2025 |
| MatText: Do Language Models Need More than Text & Scale for Materials Modeling? N Alampara, S Miret, KM Jablonka arXiv preprint arXiv:2406.17295, 2024 | 37 | 2024 |
Berend SmitEcole Polytechnique Fédérale de Lausanne (EPFL)Verified email at epfl.ch
