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.

原子模拟具有从药物设计到材料发现的广泛应用。机器学习原子间势 (MLIP) 已成为计算成本高昂的从头计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通用反应性 MLIP，即无需改装即可适用于广泛反应化学的 MLIP。在这里，我们通过凝相反应的自动采样开发了通用反应性 MLIP (ANI-1xnr)。然后，ANI-1xnr 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、甲烷燃烧以及早期地球小分子自发形成甘氨酸。在所有研究中，ANI-1xnr 与使用传统模型化学方法的实验（如果可用）和/或之前的研究非常匹配。因此，ANI-1xnr 被证明是一种针对凝聚相中的 C、H、N 和 O 元素的高度通用的反应性 MLIP，可实现硅反应化学实验的高通量。