Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields.

ABSTRACT: Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key step toward this goal has been made with the GEMS approach to biomolecular dynamics [Unke et al., Sci. Adv. 2024, 10, eadn4397]. This work introduces the SO3LR method that integrates the fast and stable SO3krates neural network for semilocal interactions with universal pairwise force fields designed for short-range repulsion, long-range electrostatics, and dispersion interactions. SO3LR is trained on a diverse set of 4 million neutral and charged molecular complexes computed at the PBE0+MBD level of quantum mechanics, ensuring broad coverage of

SUBMITTER: Kabylda A 

PROVIDER: S-EPMC12447504 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature