AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry.

ABSTRACT: Open-shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges. Most of the current machine learning interatomic potentials do not distinguish between different spin states, making them unsuitable for open-shell reactive chemistry. Here we present AIMNet2-NSE (neural spin-charge equilibration), a neural network potential that incorporates spin-charge equilibration for accurate treatment of molecules and reactions with arbitrary charge and spin multiplicities. Built upon the AIMNet2 framework, AIMNet2-NSE is trained on an extensive dataset comprising 20 million closed-shell neutral and charged molecules, 13 million o

SUBMITTER: Kalita B 

PROVIDER: S-EPMC12851018 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature