Development of neural network interatomic potentials for accelerated prediction of stable compounds
ORAL
Abstract
Construction of machine learning interatomic potentials suitable for guiding unconstrained ab initio
structure searches presents a considerable challenge, as they are required to provide an accurate
description of yet unknown configurations probed in global optimizations. We have developed an
open-source wrapper based on our MAISE package that streamlines all stages of the neural network
(NN) model parameterization. An evolutionary sampling scheme for generating reference structures
improves the NNs’ mapping of regions visited in unconstrained searches, while a stratified training
approach enables the creation of standardized NN models for multiple elements. Among first
applications of such NN potentials is the prediction of new Mg-Ca alloys thermodynamically stable
under ambient or high pressures.
structure searches presents a considerable challenge, as they are required to provide an accurate
description of yet unknown configurations probed in global optimizations. We have developed an
open-source wrapper based on our MAISE package that streamlines all stages of the neural network
(NN) model parameterization. An evolutionary sampling scheme for generating reference structures
improves the NNs’ mapping of regions visited in unconstrained searches, while a stratified training
approach enables the creation of standardized NN models for multiple elements. Among first
applications of such NN potentials is the prediction of new Mg-Ca alloys thermodynamically stable
under ambient or high pressures.
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Publication: S. Hajinazar, A. Thorn, E.D. Sandoval, S. Kharabadze, A.N. Kolmogorov
MAISE: Construction of neural network interatomic models and evolutionary structure optimization
Comput. Phys. Commun. 259, 107679 (2020)
Presenters
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Saba Kharabadze
- Binghamton University