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

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...

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Bibliographic Details
Published in:Physical chemistry chemical physics : PCCP 2019-03, Vol.21 (12), p.656-6516
Main Authors: Marques, Mário R. G, Wolff, Jakob, Steigemann, Conrad, Marques, Miguel A. L
Format: Article
Language:English
Subjects:
Computer simulation
Construction
Free energy
Gold
Heat of formation
Molecular dynamics
Neural networks
Test sets
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Summary: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. We present a practical procedure to obtain reliable and unbiased neural network based force fields for solids.
ISSN:1463-9076
1463-9084
DOI:10.1039/c8cp05771k