General many-body framework for data-driven potentials with arbitrary quantum mechanical accuracy: Water as a case study E Lambros, S Dasgupta, E Palos, S Swee, J Hu, F Paesani Journal of Chemical Theory and Computation 17 (9), 5635-5650, 2021 | 41 | 2021 |
Assessing the interplay between functional-driven and density-driven errors in DFT models of water E Palos, E Lambros, S Swee, J Hu, S Dasgupta, F Paesani Journal of Chemical Theory and Computation 18 (6), 3410-3426, 2022 | 24 | 2022 |
Density functional theory of water with the machine-learned DM21 functional E Palos, E Lambros, S Dasgupta, F Paesani The Journal of Chemical Physics 156 (16), 2022 | 12 | 2022 |
Data-driven many-body potentials from density functional theory for aqueous phase chemistry E Palos, S Dasgupta, E Lambros, F Paesani Chemical Physics Reviews 4 (1), 2023 | 8 | 2023 |
Consistent density functional theory-based description of ion hydration through density-corrected many-body representations E Palos, A Caruso, F Paesani The Journal of Chemical Physics 159 (18), 2023 | 4 | 2023 |
Balance between physical interpretability and energetic predictability in widely used dispersion-corrected density functionals S Dasgupta, E Palos, Y Pan, F Paesani Journal of Chemical Theory and Computation 20 (1), 49-67, 2023 | 2 | 2023 |
Modeling the ternary chalcogenide Na2MoSe4 from first-principles E Palos, A Reyes-Serrato, G Alonso-Nuñez, JG Sánchez Journal of Physics: Condensed Matter 33 (2), 025501, 2020 | 2 | 2020 |
Electronic structure calculations for rhenium carbonitride: An extended Hückel tight-binding study EI Palos, JI Paez, A Reyes-Serrato, DH Galván Physica Scripta 93 (11), 115801, 2018 | 1 | 2018 |
Many-body interactions and deep neural network potentials for water Y Zhai, R Rashmi, E Palos, F Paesani The Journal of Chemical Physics 160 (14), 2024 | | 2024 |
Density-Corrected Many-body Representations in Aqueous Phase Chemistry E Palos, F Paesani Bulletin of the American Physical Society, 2024 | | 2024 |