MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data

TL;DR

This paper enhances MD-GAN by incorporating multi-particle data, significantly improving long-term molecular behavior predictions from short-term MD data, especially in diffusion processes.

Contribution

The study demonstrates that using multiple particles per molecule in MD-GAN improves prediction accuracy and efficiency over single-particle inputs.

Findings

Multi-particle input accelerates diffusion prediction

Three-particle data reduces training time by two-thirds

Unobserved transitions are accurately predicted

Abstract

MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. Therefore, we investigated the effectiveness of adding information from other particles to the learning process. In the experiment of the polyethylene system, when the dynamics of three particles of each molecule were used, the diffusion was successfully predicted using one-third of the time length of the training data, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method.