Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systems.

ABSTRACT: Electronic structure methods offer in principle accurate predictions of molecular properties, however, their applicability is limited by computational costs. Empirical methods are cheaper, but come with inherent approximations and are dependent on the quality and quantity of training data. The rise of machine learning (ML) force fields (FFs) exacerbates limitations related to training data even further, especially for condensed-phase systems for which the generation of large and high-quality training datasets is difficult. Here, we propose a hybrid ML/classical FF model that is parametrized exclusively on high-quality ab initio data of dimers and monomers in vacuum but is transferable to condensed-phase systems. The proposed hybrid model combines our previous ML-parametrized classic

SUBMITTER: Thurlemann M 

PROVIDER: S-EPMC10646964 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature