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

Ryo Kawada, Katsuhiro Endo, Daisuke Yuhara, Kenji Yasuoka

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Molecular dynamics simulation is a method of investigating the behavior of molecules, which is useful for analyzing a variety of structural and dynamic properties and mechanisms of phenomena. However, the huge computational cost of large-scale and long-time simulations is an enduring problem that must be addressed. 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 [Endo et al., Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32]. 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. The effectiveness of adding information from other particles to the learning process is investigated in this study. When the dynamics of three particles of each molecule were used in the polyethylene experiment, the diffusion was successfully predicted using the training data with a time length of approximately 40%, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method. The reduced cost for the generation of training MD data achieved in this study is useful for accelerating MD-GAN.

Original languageEnglish
Pages (from-to)8446-8455
Number of pages10
JournalSoft matter
Volume18
Issue number44
DOIs
Publication statusPublished - 2022 Oct 31

ASJC Scopus subject areas

  • Chemistry(all)
  • Condensed Matter Physics

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