Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.

ABSTRACT: Atomistic simulations are routinely employed in academia and industry to study the behavior of molecules, materials, and their interfaces. Central to these simulations are force fields (FFs), whose development is challenged by intricate interatomic interactions at different spatio-temporal scales and the vast expanse of chemical space. Machine learning (ML) FFs, trained on quantum-mechanical energies and forces, have shown the capacity to achieve sub-kcal (mol^-1 Å^-1) accuracy while maintaining computational efficiency. The TEA Challenge 2023 rigorously evaluated commonly used MLFFs across diverse applications, highlighting their strengths and weaknesses. Participants trained their models using provided datasets, and the results were systematically analyzed to assess t

SUBMITTER: Poltavsky I 

PROVIDER: S-EPMC11809572 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature