| Machine-learning energy gaps of porphyrins with molecular graph representations Z Li, N Omidvar, WS Chin, E Robb, A Morris, L Achenie, H Xin The Journal of Physical Chemistry A 122 (18), 4571-4578, 2018 | 59 | 2018 |
| 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 | 51 | 2023 |
| 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 | 38 | 2021 |
| Coordination numbers for unraveling intrinsic size effects in gold-catalyzed CO oxidation S Wang, N Omidvar, E Marx, H Xin Physical Chemistry Chemical Physics 20 (9), 6055-6059, 2018 | 32 | 2018 |
| In vitro osteogenic induction of human marrow‐derived mesenchymal stem cells by PCL fibrous scaffolds containing dexamethazone‐loaded chitosan microspheres N Omidvar, F Ganji, MB Eslaminejad Journal of biomedical materials research Part A 104 (7), 1657-1667, 2016 | 26 | 2016 |
| Overcoming site heterogeneity in search of metal nanocatalysts S Wang, N Omidvar, E Marx, H Xin ACS Combinatorial Science 20 (10), 567-572, 2018 | 23 | 2018 |
| Algorithm-derived feature representations for explainable AI in catalysis N Omidvar, H Xin Trends in Chemistry 3 (12), 990-992, 2021 | 2 | 2021 |
| 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 | 1 | 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 |
| Toward Designing Active ORR Catalysts via Interpretable and Explainable Machine Learning N Omidvar Virginia Tech, 2022 | | 2022 |
| Bayesian Chemisorption Model for Adsorbate-Specific Tuning of Electrocatalysis S Wang, H Pillai, N Omidvar, H Xin 2020 Virtual AIChE Annual Meeting, 2020 | | 2020 |
| 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 |
| Role of Intra-Particle Grain Boundaries in CO2 Electroreduction N Omidvar, S Jeong, X Ye, H Xin 2019 AIChE Annual Meeting, 2019 | | 2019 |
| Orbitalwise Coordination Number in Search of Metal Nanocatalysts for Oxygen Reduction S Wang, N Omidvar, E Marx, H Xin 2019 AIChE Annual Meeting, 2019 | | 2019 |
| Developing First-Principles Based Interatomic Potentials with Bayesian Inference N Omidvar, H Xin 2019 AIChE Annual Meeting, 2019 | | 2019 |
| Machine learning for accelerating discovery of perovskite electrocatalysts H Xin, Z Li, N Omidvar, L Achenie ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | | 2019 |
| Large-Scale Exploration of Perovskites for Oxygen Evolution Via Adaptive Machine Learning Z Li, Q Zheng, N Omidvar, H Xin 2018 AIChE Annual Meeting, 2018 | | 2018 |
| A Machine Learning Model for Accelerating Biomimetic Electrocatalyst Discovery H Pillai, N Omidvar, J Luo, H Xin 2018 AIChE Annual Meeting, 2018 | | 2018 |
| Developing First-Principles Based Embedded Atom Method Potentials for Metal Clusters Using Bayesian Statistics N Omidvar, S Wang, H Xin 2018 AIChE Annual Meeting, 2018 | | 2018 |
| Overcoming Site Heterogeneity in Search of Metal Nanocatalysts for Oxygen Reduction S Wang, N Omidvar, E Marx, H Xin 2018 AIChE Annual Meeting, 2018 | | 2018 |