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This research aims to introduce a hybrid machine-learning and molecular mechanics potential for simulating large systems.

Explicit, Machine-LearnedTwo-Body Potentials forMolecular Simulations

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This research aims to introduce a hybrid machine-learning and molecular mechanics potential for simulating large systems.
Introduced ML/MM potential combining PhysNet ML method for monomers with empirical MM force field for longer-range interactions.
Developed models fitted to MP2 dimer and pairwise cluster energies; assessed at various switching distances.
Conducted energy-conserving molecular dynamics simulations for dichloromethane and acetone test systems.
Validated hybrid ML/MM potential by comparing total cluster energies with experimental data.
Demonstrated partial radial distribution functions of dichloromethane matched with experimental results.
Discussed future extensions for including higher-order many-body effects in potential.

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Randomized trial shows effectiveness of hybrid machine-learning potentials in molecular simulations, suggesting advances in computational methods.

Chaton et al. (2026) studied this question.

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Randomized trial shows effectiveness of hybrid machine-learning potentials in molecular simulations, suggesting advances in computational methods.