Constructing an ab initio DNA and solvent interaction model with equivariant neural networks
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Lee, Alex J.↗
Engineering topics
Rackers, Joshua
Publications and source records attributed to Rackers, Joshua.
Predicting accurate ab initio DNA electron densities with equivariant neural networks.
Abstract not provided.
Lee, Alex↗
Beyond the Black Box: The potential and problems of equivariant electron densities.
Abstract not provided.
Rackers, Joshua↗
Accurate hellmann-feynman forces with optimized atom-centered gaussian basis sets .
Abstract not provided.
Pathak, Shivesh↗
Predicting quantum-accurate electron densities for DNA with equivariant neural networks.
Abstract not provided.
Lee, Alex↗
equivariant_electron_density
SAND2022-3548 O This code contains scripts that allow users to generate molecular electron densities, train a machine learning model to predict electron densities, and analyze the accuracy of machine learned electron densities. It accompanies a publication titled "Cracking the Quantum Scaling Limit with Machine Learned Electron Densities.” Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Rackers, Joshua↗
Combining physics and AI for quantum-accurate simulations of biological molecules.
Abstract not provided.
Rackers, Joshua↗
Machine learning to build quantum-accurate models for biological macromolecules.
Abstract not provided.
Rackers, Joshua↗
Uncharted Territory: Mapping the Quantum World with Machine Learning.
Abstract not provided.
Rackers, Joshua↗
What can machine learning and the Hellmann-Feynman Theorem teach us about the limits of electron correlation?.
Abstract not provided.
Rackers, Joshua↗
What can machine learning teach us about the limits of electron correlation?.
Abstract not provided.
Rackers, Joshua↗