| Infusing theory into deep learning for interpretable reactivity prediction SH Wang, HS Pillai, S Wang, LEK Achenie, H Xin Nature communications 12 (1), 5288, 2021 | 84 | 2021 |
| Interpretable machine learning of chemical bonding at solid surfaces N Omidvar, HS Pillai, SH Wang, T Mou, S Wang, A Athawale, ... The Journal of Physical Chemistry Letters 12 (46), 11476-11487, 2021 | 34 | 2021 |
| Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks HS Pillai, Y Li, SH Wang, N Omidvar, Q Mu, LEK Achenie, ... Nature communications 14 (1), 792, 2023 | 32 | 2023 |
| Large scale benchmark of materials design methods K Choudhary, D Wines, K Li, KF Garrity, V Gupta, AH Romero, JT Krogel, ... arXiv preprint arXiv:2306.11688, 2023 | 12 | 2023 |
| JARVIS-Leaderboard: a large scale benchmark of materials design methods K Choudhary, D Wines, K Li, KF Garrity, V Gupta, AH Romero, JT Krogel, ... npj Computational Materials 10 (1), 93, 2024 | 7 | 2024 |
| Interpretable Machine Learning for Catalytic Materials Design toward Sustainability H Xin, T Mou, HS Pillai, SH Wang, Y Huang Accounts of Materials Research 5 (1), 22-34, 2023 | 7 | 2023 |
| Analysis of a looped high pressure steam pipeline network in a large-scale refinery SH Wang, WJ Wang, CY Chang, CL Chen Industrial & Engineering Chemistry Research 54 (37), 9222-9229, 2015 | 5 | 2015 |
| Transient response analysis of high pressure steam distribution networks in a refinery CY Chang, SH Wang, YC Huang, CL Chen 2017 6th International Symposium on Advanced Control of Industrial Processes …, 2017 | 4 | 2017 |
| Infusing theory into machine learning for interpretable reactivity prediction SH Wang, H Somarajan Pillai, S Wang, LEK Achenie, H Xin arXiv e-prints, arXiv: 2103.15210, 2021 | 2 | 2021 |
| Examining Generalizability of AI Models for Catalysis SH Wang, H Xin, L Achenie, K Choudhary | | 2024 |
| Unifying theory of electronic descriptors of metal surfaces upon perturbation Y Huang, SH Wang, M Kamanuru, LEK Achenie, JR Kitchin, H Xin Physical Review B 110 (12), L121404, 2024 | | 2024 |
| Unraveling Reactivity Origin of Oxygen Reduction at High-Entropy Alloy Electrocatalysts with a Computational and Data-Driven Approach Y Huang, SH Wang, X Wang, N Omidvar, LEK Achenie, SE Skrabalak, ... The Journal of Physical Chemistry C 128 (27), 11183-11189, 2024 | | 2024 |
| Explainable AI for optimizing oxygen reduction on Pt monolayer core–shell catalysts N Omidvar, SH Wang, Y Huang, HS Pillai, A Athawale, S Wang, ... Electrochemical Science Advances, e202300028, 2024 | | 2024 |
| Interpretable Design of Multimetallic Catalysts for Ammonia Electrooxidation with Deep Learning SH Wang, H Pillai, Y Li, L Achenie, G Wu, H Xin 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Discovery of Pt Trimetallic Electrocatalysts for Ammonia Oxidation with Interpretable Deep Learning H Pillai, Y Li, SH Wang, Q Mu, C Pokrywka, LEK Achenie, ... 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Infusing Theory into Deep Learning for Interpretable Stability Prediction of Transition Metal Alloys Y Huang, SH Wang, H Xin 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Theory-Infused Neural Network for Interpretable D-Band Moments Prediction SH Wang, Y Huang, H Pillai, L Achenie, H Xin 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Accelerating Catalytic Materials Discovery for Ammonia Electrooxidation Via Interpretable Deep Learning HS Pillai, Y Li, SH Wang, Q Mu, LEK Achenie, G Wu, H Xin The 27th North American Catalysis Society Meeting, 2022 | | 2022 |
| Accelerating Ammonia Electrooxidation Catalyst Discovery through Interpretable Machine Learning H Pillai, SH Wang, L Achenie, H Xin 2021 AIChE Annual Meeting, 2021 | | 2021 |
| Physics Informed Machine Learning of Chemisorption at Metal Surfaces SH Wang, S Wang, N Omidvar, L Achenie, H Xin 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
