publications

2024

1. 3D molecule generation by denoising voxel grids

   Pedro O O Pinheiro, Joshua Rackers, Joseph Kleinhenz, Michael Maser, Omar Mahmood, Andrew Watkins, Stephen Ra, Vishnu Sresht, and Saeed Saremi

   Advances in Neural Information Processing Systems, 2024
2. Building an ab initio solvated DNA model using Euclidean neural networks

   Alex J Lee, Joshua A Rackers, Shivesh Pathak, and William P Bricker

   Plos one, 2024
3. On the design space between molecular mechanics and machine learning force fields

   Yuanqing Wang, Kenichiro Takaba, Michael S Chen, Marcus Wieder, Yuzhi Xu, Tong Zhu, John ZH Zhang, Arnav Nagle, Kuang Yu, Xinyan Wang, and  others

   arXiv preprint arXiv:2409.01931, 2024
4. JAMUN: Transferable Molecular Conformational Ensemble Generation with Walk-Jump Sampling

   Ameya Daigavane, Bodhi P Vani, Saeed Saremi, Joseph Kleinhenz, and Joshua Rackers

   arXiv preprint arXiv:2410.14621, 2024

2023

1. Accurate Hellmann–Feynman forces from density functional calculations with augmented Gaussian basis sets

   Shivesh Pathak, Ignacio Ema López, Alex J Lee, William P Bricker, Rafael López Fernández, Susi Lehtola, and Joshua A Rackers

   The Journal of Chemical Physics, 2023
2. A recipe for cracking the quantum scaling limit with machine learned electron densities

   Joshua A Rackers, Lucas Tecot, Mario Geiger, and Tess E Smidt

   Machine Learning: Science and Technology, 2023

2022

1. Cracking the quantum scaling limit with machine learned electron densities

   Joshua A Rackers, Lucas Tecot, Mario Geiger, and Tess E Smidt

   arXiv preprint arXiv:2201.03726, 2022
2. Predicting quantum-accurate electron densities for DNA with equivariant neural networks

   Alex Lee, Joshua Rackers, and William Bricker

   2022
3. Accurate hellmann-feynman forces with optimized atom-centered gaussian basis sets.

   Shivesh Pathak, Joshua Rackers, Ema Lopez Ignacio, Lopez Fernandez Rafael, Alex J Lee, William P Bricker, and Susi Lehtola

   2022
4. Hierarchical learning in Euclidean neural networks

   Joshua A Rackers, and Pranav Rao

   arXiv preprint arXiv:2210.04766, 2022
5. Predicting accurate ab initio DNA electron densities with equivariant neural networks

   Alex J Lee, Joshua A Rackers, and William P Bricker

   Biophysical Journal, 2022
6. Classical exchange polarization: An anisotropic variable polarizability model

   Moses KJ Chung, Zhi Wang, Joshua A Rackers, and Jay W Ponder

   The Journal of Physical Chemistry B, 2022

2021

1. Thermodynamics of ion binding and occupancy in potassium channels

   Zhifeng Jing, Joshua A Rackers, Lawrence R Pratt, Chengwen Liu, Susan B Rempe, and Pengyu Ren

   Chemical Science, 2021
2. Polarizable water potential derived from a model electron density

   Joshua A Rackers, Roseane R Silva, Zhi Wang, and Jay W Ponder

   Journal of chemical theory and computation, 2021
3. Water in an external electric field: comparing charge distribution methods using ReaxFF simulations

   Jason P Koski, Stan G Moore, Raymond C Clay, Kurt A O’Hearn, H Metin Aktulga, Mark A Wilson, Joshua A Rackers, J Matthew D Lane, and Normand A Modine

   Journal of Chemical Theory and Computation, 2021
4. equivariant_electron_density

   Joshua Rackers

   2021

2019

1. Classical Pauli repulsion: An anisotropic, atomic multipole model

   Joshua A Rackers, and Jay W Ponder

   The Journal of chemical physics, 2019
2. A Physics-Based Intermolecular Potential for Biomolecular Simulation

   Joshua A Rackers

   Washington University in St. Louis, 2019

2018

1. Tinker 8: software tools for molecular design

   Joshua A Rackers, Zhi Wang, Chao Lu, Marie L Laury, Louis Lagardère, Michael J Schnieders, Jean-Philip Piquemal, Pengyu Ren, and Jay W Ponder

   Journal of chemical theory and computation, 2018
2. A physically grounded damped dispersion model with particle mesh Ewald summation

   Joshua A Rackers, Chengwen Liu, Pengyu Ren, and Jay W Ponder

   The Journal of chemical physics, 2018
3. 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, JW Ponder, and MJ Schnieders

   J. Chem. Theory Comput., 2018

2017

1. An optimized charge penetration model for use with the AMOEBA force field

   Joshua A Rackers, Qiantao Wang, Chengwen Liu, Jean-Philip Piquemal, Pengyu Ren, and Jay W Ponder

   Physical Chemistry Chemical Physics, 2017

2016

1. Scalable improvement of SPME multipolar electrostatics in anisotropic polarizable molecular mechanics using a general short-range penetration correction up to quadrupoles

   Christophe Narth, Louis Lagardère, Étienne Polack, Nohad Gresh, Qiantao Wang, David R Bell, Joshua A Rackers, Jay W Ponder, Pengyu Y Ren, and Jean-Philip Piquemal

   Journal of computational chemistry, 2016

2015

1. General model for treating short-range electrostatic penetration in a molecular mechanics force field

   Qiantao Wang, Joshua A Rackers, Chenfeng He, Rui Qi, Christophe Narth, Louis Lagardere, Nohad Gresh, Jay W Ponder, Jean-Philip Piquemal, and Pengyu Ren

   Journal of chemical theory and computation, 2015