| Open catalyst 2020 (OC20) dataset and community challenges L Chanussot, A Das, S Goyal, T Lavril, M Shuaibi, M Riviere, K Tran, ... Acs Catalysis 11 (10), 6059-6072, 2021 | 565 | 2021 |
| The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts R Tran, J Lan, M Shuaibi, BM Wood, S Goyal, A Das, J Heras-Domingo, ... ACS Catalysis 13 (5), 3066-3084, 2023 | 146 | 2023 |
| An introduction to electrocatalyst design using machine learning for renewable energy storage CL Zitnick, L Chanussot, A Das, S Goyal, J Heras-Domingo, C Ho, W Hu, ... arXiv preprint arXiv:2010.09435, 2020 | 92 | 2020 |
| Rotation invariant graph neural networks using spin convolutions M Shuaibi, A Kolluru, A Das, A Grover, A Sriram, Z Ulissi, CL Zitnick arXiv preprint arXiv:2106.09575, 2021 | 85 | 2021 |
| Forcenet: A graph neural network for large-scale quantum calculations W Hu, M Shuaibi, A Das, S Goyal, A Sriram, J Leskovec, D Parikh, ... arXiv preprint arXiv:2103.01436, 2021 | 73 | 2021 |
| GemNet-OC: developing graph neural networks for large and diverse molecular simulation datasets J Gasteiger, M Shuaibi, A Sriram, S Günnemann, Z Ulissi, CL Zitnick, ... arXiv preprint arXiv:2204.02782, 2022 | 66 | 2022 |
| Spherical channels for modeling atomic interactions L Zitnick, A Das, A Kolluru, J Lan, M Shuaibi, A Sriram, Z Ulissi, B Wood Advances in Neural Information Processing Systems 35, 8054-8067, 2022 | 65 | 2022 |
| Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery A Kolluru, M Shuaibi, A Palizhati, N Shoghi, A Das, B Wood, CL Zitnick, ... ACS Catalysis 12 (14), 8572-8581, 2022 | 44 | 2022 |
| Enabling robust offline active learning for machine learning potentials using simple physics-based priors M Shuaibi, S Sivakumar, RQ Chen, ZW Ulissi Machine Learning: Science and Technology 2 (2), 025007, 2020 | 38 | 2020 |
| AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials J Lan, A Palizhati, M Shuaibi, BM Wood, B Wander, A Das, M Uyttendaele, ... npj Computational Materials 9 (1), 172, 2023 | 33 | 2023 |
| Transfer learning using attentions across atomic systems with graph neural networks (TAAG) A Kolluru, N Shoghi, M Shuaibi, S Goyal, A Das, CL Zitnick, Z Ulissi The Journal of Chemical Physics 156 (18), 2022 | 32 | 2022 |
| How do graph networks generalize to large and diverse molecular systems J Gasteiger, M Shuaibi, A Sriram, S Günnemann, Z Ulissi, CL Zitnick, ... arXiv preprint arXiv 2204, 2022 | 18 | 2022 |
| Adsorbml: Accelerating adsorption energy calculations with machine learning J Lan, A Palizhati, M Shuaibi, BM Wood, B Wander, A Das, M Uyttendaele, ... arXiv preprint arXiv:2211.16486 1 (2), 7, 2022 | 13 | 2022 |
| Chemical Properties from Graph Neural Network-Predicted Electron Densities EM Sunshine, M Shuaibi, ZW Ulissi, JR Kitchin The Journal of Physical Chemistry C 127 (48), 23459-23466, 2023 | 12 | 2023 |
| Nima Shoghi, et al R Tran, J Lan, M Shuaibi, BM Wood, S Goyal, A Das, J Heras-Domingo, ... The open catalyst, 3066-3084, 2022 | 11 | 2022 |
| Brook Wander, Abhishek Das, Matt Uyttendaele, C Lawrence Zitnick, and Zachary W Ulissi. Adsorbml: Accelerating adsorption energy calculations with machine learning J Lan, A Palizhati, M Shuaibi, BM Wood arXiv preprint arXiv:2211.16486, 2022 | 10 | 2022 |
| Open materials 2024 (omat24) inorganic materials dataset and models L Barroso-Luque, M Shuaibi, X Fu, BM Wood, M Dzamba, M Gao, A Rizvi, ... arXiv preprint arXiv:2410.12771, 2024 | 8 | 2024 |
| CatTSunami: Accelerating Transition State Energy Calculations with Pre-trained Graph Neural Networks B Wander, M Shuaibi, JR Kitchin, ZW Ulissi, CL Zitnick arXiv preprint arXiv:2405.02078, 2024 | 5 | 2024 |
| AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification M Shuaibi, Y Hu, X Lei, BM Comer, M Adams, J Paras, RQ Chen, E Musa, ... Journal of Open Source Software 8 (87), 5035, 2023 | 4 | 2023 |
| Generalizing denoising to non-equilibrium structures improves equivariant force fields YL Liao, T Smidt, M Shuaibi, A Das arXiv preprint arXiv:2403.09549, 2024 | 3 | 2024 |
C. Lawrence ZitnickFAIR (Meta)Verified email at fb.com
