| 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 | 140 | 2019 |
| 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 | 62 | 2020 |
| Multi-objective gflownets M Jain, SC Raparthy, A Hernández-Garcıa, J Rector-Brooks, Y Bengio, ... International conference on machine learning, 14631-14653, 2023 | 45 | 2023 |
| 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 | 44 | 2023 |
| 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 | 28 | 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 | 27 | 2023 |
| Storage wars: Batteries vs. supercapacitors S Miret Berkeley Energy and Resources Collaborative, November 10, 2013 | 22 | 2013 |
| 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 | 18 | 2023 |
| 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 | 14 | 2023 |
| Optimizing memory placement using evolutionary graph reinforcement learning S Khadka, E Aflalo, M Marder, A Ben-David, S Miret, S Mannor, T Hazan, ... arXiv preprint arXiv:2007.07298, 2020 | 14 | 2020 |
| Can retriever-augmented language models reason? the blame game between the retriever and the language model P BehnamGhader, S Miret, S Reddy arXiv preprint arXiv:2212.09146, 2022 | 13 | 2022 |
| Learning intrinsic symbolic rewards in reinforcement learning HU Sheikh, S Khadka, S Miret, S Majumdar, M Phielipp 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 13 | 2022 |
| 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, 2023 | 11* | 2023 |
| The open MatSci ML toolkit: A flexible framework for machine learning in materials science S Miret, KLK Lee, C Gonzales, M Nassar, M Spellings arXiv preprint arXiv:2210.17484, 2022 | 8 | 2022 |
| Neuroevolution-enhanced multi-objective optimization for mixed-precision quantization S Miret, VS Chua, M Marder, M Phiellip, N Jain, S Majumdar Proceedings of the Genetic and Evolutionary Computation Conference, 1057-1065, 2022 | 8 | 2022 |
| Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, and Matthew Spellings. The open matsci ml toolkit: A flexible framework for machine learning in materials science S Miret arXiv preprint arXiv:2210.17484, 2022 | 7 | 2022 |
| 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 | 6 | 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 | 6 | 2024 |
| 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 | 6 | 2023 |
| 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 | 6 | 2023 |
