Doi, Hideo https://orcid.org/0000-0002-9885-1967
Matsuoka, Sota
Okuwaki, Koji https://orcid.org/0000-0002-4510-5717
Hatada, Ryo
Minami, Sojiro
Suhara, Ryosuke
Mochizuki, Yuji https://orcid.org/0000-0002-7310-5183
Funding for this research was provided by:
Rikkyo University (SFR)
Article Title: Machine learning to improve efficiency of non-empirical interaction parameter for dissipative particle dynamics (DPD) simulation
Journal Title: Japanese Journal of Applied Physics
Article Type: paper
Copyright Information: © 2023 The Japan Society of Applied Physics. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Publication dates
Date Received: 2023-04-20
Date Accepted: 2023-07-06
Online publication date: 2023-07-28