2024/11/30 by Qi Yu, Ruitao Ma, Yu, Qi +15 · 2 citations
Chemistry · Computer Science · Materials Science · #Atomic and Molecular Clusters (physics.atm-clus) #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #Computational Physics (physics.comp-ph) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Machine Learning in Materials Science #Mass Spectrometry Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2412.00522
openalex publication_date 2024/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully achieved. Here, we present a novel method that combines aspects of both approaches, and achieves state-of-the-art balance of 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. Without sophisticated neural network design, the structural descriptors of monomers are described by 1-body and 2-body effective interactions, enforced by appropriate sets of PIPs as inputs to the feed forward NN. We demonstrate the performance of this method through systematic assessments of models for gas-phase water trimer, liquid water, and also liquid CO2. The high accuracy, fast speed, and flexibility of this method provide a new route for constructing accurate ML potentials and enabling large-scale quantum and classical simulations for complex molecular systems.