MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules
arxiv(2023)
摘要
Classical empirical force fields have dominated biomolecular simulation for
over 50 years. Although widely used in drug discovery, crystal structure
prediction, and biomolecular dynamics, they generally lack the accuracy and
transferability required for predictive modelling. In this paper, we introduce
MACE-OFF23, a transferable force field for organic molecules created using
state-of-the-art machine learning technology and first-principles reference
data computed with a high level of quantum mechanical theory. MACE-OFF23
demonstrates the remarkable capabilities of local, short-range models by
accurately predicting a wide variety of gas and condensed phase properties of
molecular systems. It produces accurate, easy-to-converge dihedral torsion
scans of unseen molecules, as well as reliable descriptions of molecular
crystals and liquids, including quantum nuclear effects. We further demonstrate
the capabilities of MACE-OFF23 by determining free energy surfaces in explicit
solvent, as well as the folding dynamics of peptides. Finally, we simulate a
fully solvated small protein, observing accurate secondary structure and
vibrational spectrum. These developments enable first-principles simulations of
molecular systems for the broader chemistry community at high accuracy and low
computational cost.
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