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Center for Computing Research

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Publication	Type	Year
Predicting accurate ab initio DNA electron densities with equivariant neural networks Dna28 Alex Lee, William Bricker, Joshua Rackers	Conference Poster – 2022 Conference Poster	2022
Accurate Hellmann-Feynman forces with optimized atom-centered Gaussian basis sets Computational Materials Science and EngineeringGordon Research ConferenceComparing Theories, Algorithms and Computation Protocols in Materials Sci Shivesh Pathak, Joshua Rackers, Ignacio Ema Lpez, Rafael Lpez Fernndez, Alex J. Lee, William P. Bricker, Susi Lehtola	Conference Poster – 2022 Conference Poster	2022
Beyond the Black Box: The potential and problems of equivariant electron densities Swiss Equivariant Learning Workshop Joshua Rackers	Conference Presentation – 2022 Conference Presentation	2022
Predicting quantum-accurate electron densities for DNA with equivariant neural networks American Chemical Society Meeting Alex Lee, Joshua Rackers, William Bricker	Conference Presentation – 2022 Conference Presentation	2022
Combining physics and AI for quantum-accurate simulations of biological molecules University of New Mexico Chemical and Biological Engineering Fall Seminar Joshua Rackers	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
A Polarizable Water Potential Derived from a Model Electron Density Journal of Chemical Theory and Computation Joshua Rackers, Roseane Silva, Zhi Wang, Jay Ponder https://www.osti.gov/search/identifier:1830522	Journal Article – 2021 Journal Article	2021
Machine learning to build quantum-accurate models for biological macromolecules NM Partnership Schools LDRD Poster Session Joshua Rackers, Alex Jemyung Lee	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Uncharted Territory: Mapping the Quantum World with Machine Learning Bay Area Research SLAM Joshua Rackers	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
What can machine learning teach us about the limits of electron correlation? Non-Covalent Interactions in Large Molecules and Extended Materials Joshua Rackers	Conference Presentation – 2021 Conference Presentation	2021
What can machine learning and the Hellmann-Feynman Theorem teach us about the limits of electron correlation? American Chemical Society Fall Meeting Joshua Rackers	Conference Presentation – 2021 Conference Presentation	2021
What is Machine Learning good for anyway? Science Friday - Washington University in St. Louis Department of Biochemistry and Molecular Biophysics Joshua Rackers	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
What can you do with a polarizable force field? LAMMPS Workshop and Symposium Joshua Rackers	Conference Presentation – 2021 Conference Presentation	2021
Thermodynamics of ion binding and occupancy in potassium channels Chemical Science Susan Rempe, Zhifeng Jing, Joshua Rackers, Lawrence Pratt, Chengwen Liu, Pengyu Ren https://www.osti.gov/search/identifier:1810365	Journal Article – 2021 Journal Article	2021
Combining physics and AI to understand the behavior of molecules Computational Research Leadership Council seminar series Joshua Rackers https://www.osti.gov/search/identifier:1870584	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
The Hellmann-Feynman Theorem, Revisited PsiCon 2020 Joshua Rackers https://www.osti.gov/search/identifier:1835183	Conference Presentation – 2020 Conference Presentation	2020
Predicting the Behavior of Biomolecules with Quantum Physics UNM Biology Department Seminar Joshua Rackers https://www.osti.gov/search/identifier:1821311	Presentation (non-conference) – 2020 Presentation (non-conference)	2020
Document Title	Type	Year