美国洛斯阿拉莫斯国家实验室Smith, Justin S.团队探索了具有通用反应性机器学习潜力的凝聚相化学前沿。相关研究成果于2024年3月7日发表于国际顶尖学术期刊《自然—化学》。

原子模拟从药物设计到材料发现有着广泛的应用。机器学习原子间势（MLIPs）已成为计算成本高昂的从头算模拟的有效替代方案。因此，化学和材料科学将极大地受益于一般的反应性MLIP，即适用于广泛反应性化学而无需改装的MLIP。

该文中，研究人员通过冷凝相反应的自动采样开发了一种通用的反应性MLIP（ANI-1xnr）。然后将ANI-1xnr应用于研究五个不同的系统：碳固相成核、乙炔形成石墨烯环、生物燃料添加剂、甲烷燃烧和早期地球小分子自发形成甘氨酸。

在所有研究中，ANI-1xnr与实验（如有）和/或使用传统模型化学方法的先前研究非常匹配。因此，ANI-1xnr被证明是凝聚相中C、H、N和O元素的高度通用反应性MLIP，从而实现了硅反应性化学实验的高通量。

附：英文原文

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

Author: Zhang, Shuhao, Mako, Magorzata Z., Jadrich, Ryan B., Kraka, Elfi, Barros, Kipton, Nebgen, Benjamin T., Tretiak, Sergei, Isayev, Olexandr, Lubbers, Nicholas, Messerly, Richard A., Smith, Justin S.

Issue&Volume: 2024-03-07

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.

DOI: 10.1038/s41557-023-01427-3

Source: https://www.nature.com/articles/s41557-023-01427-3