Issue 12, 2019

Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures

Abstract

We present a practical procedure to obtain reliable and unbiased neural network based force fields for solids. Training and test sets are efficiently generated from global structural prediction runs, at the same time assuring the structural variety and importance of sampling the relevant regions of phase space. The neural networks are trained to yield not only good formation energies, but also accurate forces and stresses, which are the quantities of interest for molecular dynamics simulations. Finally, we construct, as an example, several force fields for both semiconducting and metallic elements, and prove their accuracy for a variety of structural and dynamical properties. These are then used to study the melting of bulk copper and gold.

Graphical abstract: Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures

Supplementary files

Article information

Article type
Paper
Submitted
12 Sep 2018
Accepted
25 Feb 2019
First published
07 Mar 2019

Phys. Chem. Chem. Phys., 2019,21, 6506-6516

Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures

M. R. G. Marques, J. Wolff, C. Steigemann and M. A. L. Marques, Phys. Chem. Chem. Phys., 2019, 21, 6506 DOI: 10.1039/C8CP05771K

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