TY - JOUR
T1 - Benchmarking Universal Machine Learning Force Fields with Hydrogen-Bonding Cooperativity
AU - Feng , Xinping
AU - Xu , You
AU - Huang , Jing
JO - Communications in Computational Chemistry
VL - 2
SP - 152
EP - 160
PY - 2025
DA - 2025/06
SN - 7
DO - http://doi.org/10.4208/cicc.2025.90.02
UR - https://global-sci.org/intro/article_detail/cicc/24185.html
KW - Machine learning force field, cooperative effects, self-assembly, neural network potential, hydrogen bond.
AB - <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>