Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
Shuhao Zhang, Małgorzata Z. Makoś, Ryan B. Jadrich, Elfi Kraka, Kipton Barros, Benjamin T. Nebgen, Sergei Tretiak, Olexandr Isayev, Nicholas Lubbers, Richard A. Messerly, +1 more
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Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.
Abstract
Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations. For this reason, chemistry and materials science would greatly benefit from a general reactive MLIP, that is, an MLIP that is applicable to a broad range of reactive chemistry without the need for refitting. Here we develop a general reactive MLIP (ANI-1xnr) through automated sampling of condensed-phase reactions. ANI-1xnr is then applied to study five distinct systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early earth small molecules. In all studies, ANI-1xnr closely matches experiment (when available) and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP for C, H, N and O elements in the condensed phase, enabling high-throughput in silico reactive chemistry experimentation.
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Cite This Paper
@article{Zhang2024,
author = {Zhang, Shuhao and Makoś, Małgorzata Z. and Jadrich, Ryan B. and Kraka, Elfi and Barros, Kipton and Nebgen, Benjamin T. and Tretiak, Sergei and Isayev, Olexandr and Lubbers, Nicholas and Messerly, Richard A. and Smith, Justin S.},
title = {Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential},
year = {2024},
journal = {Nature Chemistry},
volume = {16},
number = {5},
pages = {727--734},
doi = {10.1038/s41557-023-01427-3},
url = {http://dx.doi.org/10.1038/s41557-023-01427-3},
publisher = {Springer Science and Business Media LLC},
keywords = {machine learning, ab initio, high-throughput},
researchAreas = {ml-potentials, experiment-automation},
highlight = {Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.},
citations = {85}
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