A neural network interface for DL_POLY and its application to liquid water. Issue 2 (11th February 2021)

• Record Type: Journal Article
• Authors: Sukuba, Ivan; Chen, Lei; Probst, Michael; Kaiser, Alexander
• Is Part Of: Molecular simulation. Volume 47:Issue 2/3(2021)
• Page Start: 113
• Page End: 118
• Publication Date: 2021-02-11
• Subjects: Feedforward neural network -- molecular dynamics simulation -- liquid water simulation; Molecular dynamics -- Computer simulation -- Periodicals; Statistical mechanics -- Computer simulation -- Periodicals; 539.6
• DOI: 10.1080/08927022.2018.1560440
• Languages: English
• ISSNs: 0892-7022

Abstract

ABSTRACT: After a general discussion of neural networks potential energy functions and their standing within the various approaches of representing the potential energy function of a system, we describe a new interface between the open source atomistic library aenet of Artrith and Urban and the DL_POLY 4 code. As an application example, the training of a neural network for liquid water is described and the network is used in a molecular dynamics simulation. The resulting thermodynamic properties are compared with those from a reference simulation with the same SPC/E model that has been used in the training.