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. (Figure presented.)
| Original language | English |
|---|---|
| Pages (from-to) | 727-734 |
| Number of pages | 8 |
| Journal | Nature Chemistry |
| Volume | 16 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2024 |
| Externally published | Yes |
Funding
The authors thank A. E. Roitberg for useful discussions on validating MLIPs for reactive chemistry. S.Z., K.B., B.T.N., S.T., N.L. and R.A.M. acknowledge support from the US Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division under Triad National Security, LLC (\u2018Triad\u2019) contract grant 89233218CNA000001 (FWP: LANLE3F2). M.Z.M. gratefully acknowledges the resources of the Los Alamos National Laboratory (LANL) Applied Machine Learning summer student programme. The work at LANL was supported by the LANL Directed Research and Development Funds 20210087DR. Work at LANL was performed in part at the Center for Nonlinear Studies and the Center for Integrated Nanotechnologies, a US Department of Energy Office of Science user facility at LANL. This research used resources provided by the LANL Institutional Computing Program. O.I. acknowledges support from Office of Naval Research through Energetic Materials Program (MURI grant number N00014-21-1-2476). M.Z.M. and E.K. acknowledge funding from National Science Foundation, grant CHE 2102461.
