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

Igor Poltavskyi, Mirela Puleva, Anton Charkin-Gorbulin, Grégory Fonseca, Ilyes Batatia, Nicholas J. Browning, Stefan Chmiela, Mengnan Cui, Thorben Frank, Stefan Heinen, Bing Huang, Silvan Käser, Adil Kabylda, Danish Khan, Carolin Müller, Alastair James Arthur Price, Kai Riedmiller, Kai Töpfer, Tsz Wai Ko, Markus Meuwly et al. (6 additional authors not shown)

Keywords: machine learning, force fields, atomistic simulations

TL;DR: Evaluation of cutting-edge MLFF models by analysing their performance across a diverse range of challenges.

Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 8th 2024, I specify the DOI in the field below, and I submit the camera-ready version of the paper.

DOI: https://doi.org/10.1039/D4SC06530A

Submission Number: 63