Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.

ABSTRACT: We present the second part of the rigorous evaluation of modern machine learning force fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of the performance of MACE, SO3krates, sGDML, SOAP/GAP, and FCHL19* in modeling molecules, molecule-surface interfaces, and periodic materials. We compare observables obtained from molecular dynamics (MD) simulations using different MLFFs under identical conditions. Where applicable, density-functional theory (DFT) or experiment serves as a reference to reliably assess the performance of the ML models. In the absence of DFT benchmarks, we conduct a comparative analysis based on results from various MLFF architectures. Our findings indicate that, at the current stage of MLFF development, the choice of ML model is in the

SUBMITTER: Poltavsky I 

PROVIDER: S-EPMC11791520 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature