@Article{CiCC-7-152,
author = {Feng , XinpingXu , You and Huang , Jing},
title = {Benchmarking Universal Machine Learning Force Fields with Hydrogen-Bonding Cooperativity},
journal = {Communications in Computational Chemistry},
year = {2025},
volume = {7},
number = {2},
pages = {152--160},
abstract = {<p style="text-align: justify;">Machine learning force fields (MLFFs) offer a promising balance between quantum mechanical (QM) accuracy and
molecular mechanics efficiency. While MLFFs have shown strong performance in modeling short-range interactions and
reproducing potential energy surfaces, their ability to capture long-range cooperative effects remains underexplored. In this study,
we assess the ability of three MLFF models — ANI, MACE-OFF, and Orb — to reproduce cooperative interactions arising from
environmental induction and dispersion, which are essential for many biomolecular processes. Using a recently proposed
framework, we quantify hydrogen bond (H-bond) cooperativity in N-methylacetamide polymers. Our results show that all MLFFs
capture cooperativity to some extent, with MACE-OFF yielding the closest agreement with QM data. These findings highlight
the importance of evaluating many-body effects in MLFFs and suggest that H-bond cooperativity can serve as a useful benchmark
for improving their physical fidelity.</p>},
issn = {2617-8575},
doi = {https://doi.org/10.4208/cicc.2025.90.02},
url = {http://global-sci.org/intro/article_detail/cicc/24185.html}
}