High-throughput screening of metal–organic frameworks for ethane–ethylene separation using the machine learning technique P Halder, JK Singh Energy & Fuels 34 (11), 14591-14597, 2020 | 39 | 2020 |
Fusing a machine learning strategy with density functional theory to hasten the discovery of 2D MXene-based catalysts for hydrogen generation BM Abraham, P Sinha, P Halder, JK Singh Journal of Materials Chemistry A 11 (15), 8091-8100, 2023 | 37 | 2023 |
Understanding adsorption of CO 2, N 2, CH 4 and their mixtures in functionalized carbon nanopipe arrays P Halder, M Maurya, SK Jain, JK Singh Physical Chemistry Chemical Physics 18 (20), 14007-14016, 2016 | 26 | 2016 |
Building unit extractor for metal–organic frameworks P Halder, Prerna, JK Singh Journal of Chemical Information and Modeling 61 (12), 5827-5840, 2021 | 8 | 2021 |
Screening of Hypothetical Metal–Organic Frameworks for Xylene Isomers and Ethylbenzene Separation P Halder, JK Singh Energy & Fuels 37 (3), 2230-2236, 2023 | 4 | 2023 |
Fusing machine learning strategy with density functional theory to hasten the discovery of MXenes for hydrogen generation BM Abraham, P Sinha, P Halder, JK Singh arXiv preprint arXiv:2212.05213, 2022 | 1 | 2022 |
Revolutionizing Hydrogen Generation with MXenes: The Role of Machine Learning in Designing Efficient Catalysts A Bokinala, P Sinha, P Halder, J Singh Bulletin of the American Physical Society, 2024 | | 2024 |