| Collaborative evolutionary reinforcement learning S Khadka, S Majumdar, T Nassar, Z Dwiel, E Tumer, S Miret, Y Liu, ... International conference on machine learning, 3341-3350, 2019 | 179 | 2019 |
| Protst: Multi-modality learning of protein sequences and biomedical texts M Xu, X Yuan, S Miret, J Tang International Conference on Machine Learning, 38749-38767, 2023 | 175 | 2023 |
| A hitchhiker's guide to geometric gnns for 3d atomic systems A Duval, SV Mathis, CK Joshi, V Schmidt, S Miret, FD Malliaros, T Cohen, ... arXiv preprint arXiv:2312.07511, 2023 | 133 | 2023 |
| 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 |
| Multi-objective gflownets M Jain, SC Raparthy, A Hernández-Garcıa, J Rector-Brooks, Y Bengio, ... International conference on machine learning, 14631-14653, 2023 | 115 | 2023 |
| Faenet: Frame averaging equivariant gnn for materials modeling AA Duval, V Schmidt, A Hernández-Garcıa, S Miret, FD Malliaros, ... International Conference on Machine Learning, 9013-9033, 2023 | 107 | 2023 |
| Evolutionary reinforcement learning for sample-efficient multiagent coordination S Majumdar, S Khadka, S Miret, S McAleer, K Tumer International Conference on Machine Learning, 6651-6660, 2020 | 90 | 2020 |
| Group SELFIES: a robust fragment-based molecular string representation AH Cheng, A Cai, S Miret, G Malkomes, M Phielipp, A Aspuru-Guzik Digital Discovery 2 (3), 748-758, 2023 | 80 | 2023 |
| Are large language models superhuman chemists? A Mirza, N Alampara, S Kunchapu, M Ríos-García, B Emoekabu, ... arXiv preprint arXiv:2404.01475, 2024 | 79 | 2024 |
| ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories M Sim, MG Vakili, F Strieth-Kalthoff, H Hao, RJ Hickman, S Miret, ... Matter 7 (9), 2959-2977, 2024 | 78 | 2024 |
| Are llms ready for real-world materials discovery? S Miret, NM Krishnan arXiv preprint arXiv:2402.05200, 2024 | 68 | 2024 |
| MatSci-NLP: Evaluating scientific language models on materials science language tasks using text-to-schema modeling Y Song, S Miret, B Liu arXiv preprint arXiv:2305.08264, 2023 | 57 | 2023 |
| 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 | 56 | 2025 |
| Honeycomb: A flexible llm-based agent system for materials science H Zhang, Y Song, Z Hou, S Miret, B Liu arXiv preprint arXiv:2409.00135, 2024 | 52 | 2024 |
| EGraFFBench: evaluation of equivariant graph neural network force fields for atomistic simulations V Bihani, S Mannan, U Pratiush, T Du, Z Chen, S Miret, M Micoulaut, ... Digital Discovery 3 (4), 759-768, 2024 | 37 | 2024 |
| Can retriever-augmented language models reason? the blame game between the retriever and the language model P BehnamGhader, S Miret, S Reddy Findings of the Association for Computational Linguistics: EMNLP 2023, 15492 …, 2023 | 37 | 2023 |
| HoneyBee: Progressive instruction finetuning of large language models for materials science Y Song, S Miret, H Zhang, B Liu arXiv preprint arXiv:2310.08511, 2023 | 37 | 2023 |
| MatText: Do language models need more than text & scale for materials modeling? N Alampara, S Miret, KM Jablonka arXiv preprint arXiv:2406.17295, 2024 | 36 | 2024 |
| Matsciml: A broad, multi-task benchmark for solid-state materials modeling KLK Lee, C Gonzales, M Nassar, M Spellings, M Galkin, S Miret arXiv preprint arXiv:2309.05934, 2023 | 30 | 2023 |
| Towards equilibrium molecular conformation generation with GFlowNets A Volokhova, M Koziarski, A Hernández-García, CH Liu, S Miret, P Lemos, ... Digital Discovery 3 (5), 1038-1047, 2024 | 24 | 2024 |
