articleNature ChemistryMar 7, 2024HYBRID OA

Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential

SZShuhao ZhangMZMałgorzata Z. MakośRBRyan B. JadrichEKElfi KrakaKBKipton Barros

Los Alamos National Laboratory · Carnegie Mellon University · +3 more institutions

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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…

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116
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100%
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11

Topics & keywords

Topics
Keywords
  • Chemistry
  • Reactive intermediate
  • Nanotechnology
  • Computational chemistry
  • Organic chemistry
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