化学
反应中间体
纳米技术
计算化学
有机化学
材料科学
催化作用
作者
Shuhao Zhang,Małgorzata Makoś,Ryan B. Jadrich,Elfi Kraka,Kipton Barros,Benjamin Nebgen,Sergei Tretiak,Olexandr Isayev,Nicholas Lubbers,Richard A. Messerly,Justin S. Smith
出处
期刊:Nature Chemistry
[Springer Nature]
日期:2024-03-07
卷期号:16 (5): 727-734
被引量:28
标识
DOI:10.1038/s41557-023-01427-3
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI