A neural network interface for DL_POLY and its application to liquid water. Issue 2 (11th February 2021) - Sukuba, Ivan; Chen, Lei; Probst, Michael; Kaiser, Alexander
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.