Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics.

ABSTRACT: Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calcu

SUBMITTER: Zhou G 

PROVIDER: S-EPMC9271210 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature