MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data
This work addresses the challenge of accelerating molecular simulations for researchers in computational chemistry, though it appears incremental by building on prior single-particle methods.
The authors tackled the problem of predicting long-time molecular dynamics from short-time data by enhancing MD-GAN with multi-particle inputs, achieving successful diffusion prediction using only one-third of the training data length compared to single-particle inputs and predicting unobserved transitions.
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.