| 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 | 197 | 2021 |
| 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 | 40 | 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 | 39 | 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 | 29 | 2021 |
| 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 | 22 | 2020 |
| 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 | 19 | 2023 |
| 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 | 13 | 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 e-prints, arXiv: 2204.02782, 2022 | 12 | 2022 |
| 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), 184702, 2022 | 11 | 2022 |
| 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 | 11 | 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 | 7 | 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, 2022 | 3 | 2022 |
| The Open Catalyst Challenge 2021: Competition Report A Das, M Shuaibi, A Palizhati, S Goyal, A Grover, A Kolluru, J Lan, A Rizvi, ... NeurIPS 2021 Competitions and Demonstrations Track, 29-40, 2022 | 1 | 2022 |
| ForceNet: A Graph Neural Network for Large-Scale Quantum Chemistry Simulation W Hu, M Shuaibi, A Das, S Goyal, A Sriram, J Leskovec, D Parikh, ... | 1 | |
| AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials J Lan, A Palizhati, M Shuaibi, B Wood, B Wander, A Das, M Uyttendaele, ... | | 2023 |
| Generalizable Machine Learning Models for Electrocatalyst Discovery M Shuaibi Carnegie Mellon University, 2022 | | 2022 |
| Accelerating on-the-Fly Active Learning of Catalyst Simulations Using Large Scale Pretrained Models J Musielewicz, M Shuaibi, Z Ulissi 2021 AIChE Annual Meeting, 2021 | | 2021 |
| An Active Learning Framework for Accelerating Saddle Point Searches Applied to Propylene Epoxidation S Sivakumar, Z Ulissi, M Shuaibi, M Adams 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
| The Open Catalyst Project Dataset L Chanussot, A Das, J Heras-Domingo, S Goyal, C Ho, T Lavril, ... 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
Zachary W. UlissiMeta (FAIR), Assoc. Prof (on leave), CMU ChemE在 andrew.cmu.edu 的电子邮件经过验证
