[21p-D901-1~19] 12.6 Nanobiotechnology

6:45 PM - 7:00 PM JST(9:45 AM - 10:00 AM UTC)

[21p-D901-19] Non-Empirical calculation of parameters for DPD simulation with the aid of machine learning

〇Hideo Doi¹, Sota Matsuoka¹, Koji Okuwaki¹, Ryo Hatada¹, Sojiro Minami¹, Ryosuke Suhara¹, Yuji Mochizuki^1,2 (1.Rikkyo Univ., 2.Univ. Tokyo)

Keywords:

Dissipative particle dynamics simulation,FMO,Machine learning

In response to the demand for highly functional materials in materials science and drug discovery, the need to predict and control mesoscale structures and behaviors has become increasingly important. However, dissipative particle dynamics (DPD) simulation requires parameters to represent molecular interactions, which poses a challenge in parameter development due to the time-consuming calculation of interaction energies between molecules. To solve this problem, we have explored the application of machine learning to replace the calculation of interaction energies. The goal is to accelerate the parameter development process and reduce the computational time.