DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

• Published in: Computer physics communications Vol. 228; no. C; pp. 178 - 184
• Main Authors: Wang, Han, Zhang, Linfeng, Han, Jiequn, E, Weinan
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.07.2018; Elsevier
• Subjects: Computer Science; Deep neural networks; Many-body potential energy; Molecular dynamics; Physics; Molecular dynamics; Many-body potential energy; Deep neural networks

Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required...