Machine-Learning Interatomic Potentials for Long-Range Systems
This addresses a bottleneck in molecular simulations for researchers, offering a versatile framework to improve accuracy, though it appears incremental as it builds on existing machine-learning potential methods.
The paper tackles the problem of machine-learning interatomic potentials often neglecting long-range interactions in molecular simulations, proposing a Sum-of-Gaussians Neural Network (SOG-Net) that integrates these interactions with close-to-linear computational complexity and demonstrates effectiveness across various systems.
Machine-learning interatomic potentials have emerged as a revolutionary class of force-field models in molecular simulations, delivering quantum-mechanical accuracy at a fraction of the computational cost and enabling the simulation of large-scale systems over extended timescales. However, they often focus on modeling local environments, neglecting crucial long-range interactions. We propose a Sum-of-Gaussians Neural Network (SOG-Net), a lightweight and versatile framework for integrating long-range interactions into machine learning force field. The SOG-Net employs a latent-variable learning network that seamlessly bridges short-range and long-range components, coupled with an efficient Fourier convolution layer that incorporates long-range effects. By learning sum-of-Gaussians multipliers across different convolution layers, the SOG-Net adaptively captures diverse long-range decay behaviors while maintaining close-to-linear computational complexity during training and simulation via non-uniform fast Fourier transforms. The method is demonstrated effective for a broad range of long-range systems.