人工知能が化学のフロンティアを開拓する(Artificial intelligence helps explore chemistry frontiers)

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2024-03-21 ロスアラモス国立研究所(LANL)

ロスアラモス国立研究所の研究チームが開発した機械学習相互原子ポテンシャルは、分子のエネルギーと原子に作用する力を予測し、既存の計算方法と比較して時間と費用を節約するシミュレーションを可能にしています。この革新的なモデルは、物理ベースの計算モデルと機械学習を組み合わせ、高速性、精度、汎用性を兼ね備えており、大規模な反応性分子シミュレーションに適しています。

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一般的な反応性機械学習の可能性で凝縮相化学のフロンティアを探る 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 & Justin S. Smith
Nature Chemistry  Published:07 March 2024
DOI:https://doi.org/10.1038/s41557-023-01427-3

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.

人工知能が化学のフロンティアを開拓する(Artificial intelligence helps explore chemistry frontiers)

1700応用理学一般
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