| Tinker 8: software tools for molecular design JA Rackers, Z Wang, C Lu, ML Laury, L Lagardère, MJ Schnieders, ... Journal of chemical theory and computation 14 (10), 5273-5289, 2018 | 496 | 2018 |
| General model for treating short-range electrostatic penetration in a molecular mechanics force field Q Wang, JA Rackers, C He, R Qi, C Narth, L Lagardere, N Gresh, ... Journal of chemical theory and computation 11 (6), 2609-2618, 2015 | 114 | 2015 |
| An optimized charge penetration model for use with the AMOEBA force field JA Rackers, Q Wang, C Liu, JP Piquemal, P Ren, JW Ponder Physical Chemistry Chemical Physics 19 (1), 276-291, 2017 | 89 | 2017 |
| Classical Pauli repulsion: An anisotropic, atomic multipole model JA Rackers, JW Ponder The Journal of chemical physics 150 (8), 2019 | 77 | 2019 |
| Scalable improvement of SPME multipolar electrostatics in anisotropic polarizable molecular mechanics using a general short‐range penetration correction up to quadrupoles C Narth, L Lagardère, É Polack, N Gresh, Q Wang, DR Bell, JA Rackers, ... Journal of computational chemistry 37 (5), 494-506, 2016 | 35 | 2016 |
| Polarizable water potential derived from a model electron density JA Rackers, RR Silva, Z Wang, JW Ponder Journal of chemical theory and computation 17 (11), 7056-7084, 2021 | 31 | 2021 |
| Thermodynamics of ion binding and occupancy in potassium channels Z Jing, JA Rackers, LR Pratt, C Liu, SB Rempe, P Ren Chemical Science 12 (25), 8920-8930, 2021 | 31 | 2021 |
| A physically grounded damped dispersion model with particle mesh Ewald summation JA Rackers, C Liu, P Ren, JW Ponder The Journal of chemical physics 149 (8), 2018 | 23 | 2018 |
| A recipe for cracking the quantum scaling limit with machine learned electron densities JA Rackers, L Tecot, M Geiger, TE Smidt Machine Learning: Science and Technology 4 (1), 015027, 2023 | 20 | 2023 |
| TINKER 8: a modular software package for molecular design and simulation JA Rackers, ML Laury, C Lu, Z Wang, L Lagardère, JP Piquemal, P Ren, ... J. Chem. Theory Comput. 14, 5273-5289, 2018 | 18 | 2018 |
| Water in an external electric field: comparing charge distribution methods using ReaxFF simulations JP Koski, SG Moore, RC Clay, KA O’Hearn, HM Aktulga, MA Wilson, ... Journal of Chemical Theory and Computation 18 (1), 580-594, 2021 | 11 | 2021 |
| Predicting accurate ab initio DNA electron densities with equivariant neural networks AJ Lee, JA Rackers, WP Bricker Biophysical Journal 121 (20), 3883-3895, 2022 | 9 | 2022 |
| Accurate Hellmann–Feynman forces from density functional calculations with augmented Gaussian basis sets S Pathak, IE López, AJ Lee, WP Bricker, RL Fernández, S Lehtola, ... The Journal of Chemical Physics 158 (1), 2023 | 5 | 2023 |
| Cracking the quantum scaling limit with machine learned electron densities JA Rackers, L Tecot, M Geiger, TE Smidt arXiv preprint arXiv:2201.03726, 2022 | 4 | 2022 |
| 3D molecule generation by denoising voxel grids PO O Pinheiro, J Rackers, J Kleinhenz, M Maser, O Mahmood, A Watkins, ... Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Classical Exchange Polarization: An Anisotropic Variable Polarizability Model MKJ Chung, Z Wang, JA Rackers, JW Ponder The Journal of Physical Chemistry B 126 (39), 7579-7594, 2022 | 3 | 2022 |
| Accurate hellmann-feynman forces with optimized atom-centered gaussian basis sets. S Pathak, J Rackers, EL Ignacio, LF Rafael, AJ Lee, WP Bricker, S Lehtola Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022 | 2 | 2022 |
| Hierarchical learning in Euclidean neural networks JA Rackers, P Rao arXiv preprint arXiv:2210.04766, 2022 | 1 | 2022 |
| Predicting quantum-accurate electron densities for DNA with equivariant neural networks A Lee, J Rackers, W Bricker | | 2022 |
| equivariant_electron_density J Rackers Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
Jay PonderDepartment of Chemistry, Washington UniversityVerified email at dasher.wustl.edu
