Efficient Machine Learning Force Field for Large-Scale Molecular Simulations of Organic Systems

TL;DR

This paper introduces a universal, efficient machine learning force field that accurately models large-scale organic systems, overcoming previous limitations in long-range interactions, stability, and computational efficiency for molecular simulations.

Contribution

The authors develop a multiscale equivariant model with active learning that significantly improves accuracy, speed, and stability in large-scale organic molecular simulations.

Findings

Achieves highest predictive accuracy among equivariant models

Extends high-precision simulations to systems with hundreds of thousands of atoms

Reduces computational speed and memory usage by magnitude-level improvements

Abstract

To address the computational challenges of ab initio molecular dynamics and the accuracy limitations of empirical force fields, the introduction of machine learning force fields has proven effective in various systems including metals and inorganic materials. However, in large-scale organic systems, the application of machine learning force fields is often hindered by impediments such as the complexity of long-range intermolecular interactions and molecular conformations, as well as the instability in long-time molecular simulations. Therefore, we propose a universal multiscale higher-order equivariant model combined with active learning techniques, efficiently capturing the complex long-range intermolecular interactions and molecular conformations. Compared to existing equivariant models, our model achieves the highest predictive accuracy, and magnitude-level improvements in computational speed and memory efficiency. In addition, a bond length stretching method is designed to improve the stability of long-time molecular simulations. Utilizing only 901 samples from a dataset with 120 atoms, our model successfully extends high precision to systems with hundreds of thousands of atoms. These achievements guarantee high predictive accuracy, fast simulation speed, minimal memory consumption, and robust simulation stability, satisfying the requirements for high-precision and long-time molecular simulations in large-scale organic systems.