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

17 April 2023, Version 3
This content is a preprint and has not undergone peer review at the time of posting.


Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive quantum chemistry simulations. In practice, developing reactive MLIPs requires prior knowledge of reaction networks to generate fitting data and refitting to extensive datasets for each new application. In this work, we develop a general reactive MLIP through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting potential (ANI-1xnr) is then applied to study five distinct condensed phase reactive chemistry 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 and/or previous studies using traditional model chemistry methods. As such, ANI-1xnr proves to be a highly general reactive MLIP that does not need to be refit for each application, enabling high-throughput in silico reactive chemistry experimentation.


Reactive molecular dynamics
Reactive force field
Reaction mechanisms
neural network potential
machine learning potential
active learning


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