Atomic decomposition; Body representations; Condensed phase; Energy; Forcefields; Invariant polynomials; Learning potential; Machine-learning; Many body; Permutationally invariant; Computer Science (miscellaneous); Computer Science Applications; Computer Networks and Communications
Abstract :
[en] Most widely used machine learning potentials for condensed-phase applications rely on many-body permutationally invariant polynomial or atom-centered neural networks. However, these approaches face challenges in achieving chemical interpretability in atomistic energy decomposition and fully matching the computational efficiency of traditional force fields. Here we present a method that combines aspects of both approaches and balances accuracy and force-field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. The structural descriptors of monomers are described by one-body and two-body effective interactions, enforced by appropriate sets of permutationally invariant polynomials as inputs to the feed-forward neural networks. Systematic assessments of models for gas-phase water trimer, liquid water, methane-water cluster and liquid carbon dioxide are performed. The improved accuracy, efficiency and flexibility of this method have promise for constructing accurate machine learning potentials and enabling large-scale quantum and classical simulations for complex molecular systems.
Disciplines :
Chemistry
Author, co-author :
Yu, Qi ; Department of Chemistry, Fudan University, Shanghai, China. qi_yu@fudan.edu.cn ; Shanghai Innovation Institute, Shanghai, China. qi_yu@fudan.edu.cn
Ma, Ruitao; Department of Chemistry, Fudan University, Shanghai, China
Q.Y. and D.H.Z. acknowledge the support from National Natural Science Foundation of China (grant numbers 22473030 and 22288201). J.M.B. acknowledges support from NASA grant (80NSSC22K1167). R.C. thanks Universit\u00E0 degli Studi di Milano for financial support under grant PSR2022_DIP_005_PI_RCONT.
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