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.
Shandong Xiehe University（山东协和学院）；Graduate School of Advanced Science and Engineering, Hiroshima University（广岛大学高等科学与工程学院）；Graduate School of Informatics, Nagoya University（名古屋大学信息学研究科）
Molecular Generative Adversarial Network with Multi-Property Optimization
Cloud Computing Technology and Application Department, Shandong Institute of Commerce and Technology（山东商业科技学院云计算技术与应用系）；Graduate School of Advanced Science and Engineering, Hiroshima University（广岛大学研究生院）；D3 Center, The University of Osaka（大阪大学D3中心）；Graduate School of Informatics, Nagoya University（名古屋大学研究生院）
A Reinforcement Learning-Driven Transformer GAN for Molecular Generation
D3 Center（D3中心）；Osaka University（大阪大学）；Graduate School of Advanced Science and Engineering（先进科学与工程研究生院）；Hiroshima University（广岛大学）；Centre for Cyber Resilience and Trust（网络韧性与信任中心）；Deakin University（德金大学）；Graduate School of Informatics（信息科学研究生院）；Nagoya University（名古屋大学）
