Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.

ABSTRACT: The recent development of machine learning (ML) methods for quantum chemistry has tremendously boosted the efficiency of molecular calculations. In this work, we use ML to enable nonadiabatic molecular dynamics (NAMD) simulations without access to the analytical energy gradients from the underlying target electronic structure method. By fine-tuning our foundational model for excited states, OMNI-P2x, on energies alone and leveraging automatic differentiability to obtain forces, we eliminate the gradient computation bottleneck that restricts calculations, such as NAMD, to methods with available analytical derivatives. First, we validate the method on the benchmark system, fulvene, demonstrating that gradient-free ML potentials accurately reproduce NAMD populations and dynamics across multip

SUBMITTER: Martyka M 

PROVIDER: S-EPMC12857564 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature