Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery.<br/>Machine learning interatomic potentials (MLIP) have become an efficient alternative to computationally expensive quantum chemistry simulations. In practice, reactive MLIPs require refitting to extensive datasets for each new application, and prior knowledge of reaction networks is required to generate fitting data. In this work, we develop a general reactive MLIP through unbiased active learning with reactive simulations. The resulting potential is then applied to study five distinct condensed-phase reactive chemistry problems: 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. Our method enables high-throughput in silico reactive chemistry experimentation

Discovering chemistry with a transferable reactive machine learning potential

Where thousands of chemistry professionals meet to share ideas and advance scientific and technical knowledge. Spring 2023 abstracts, posters, and presentations will launch at the end of March.

ACS Spring 2023

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.