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Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case Study.

ABSTRACT: Computer simulations of alloys' properties often require calculations in a large space of configurations in a supercell of the crystal structure. A common approach is to map density functional theory results into a simplified interaction model using so-called cluster expansions, which are linear on the cluster correlation functions. Alternative descriptors have not been sufficiently explored so far. We show here that a simple descriptor based on the Coulomb matrix eigenspectrum clearly outperforms the cluster expansion for both total energy and bandgap energy predictions in the configurational space of a MgO-ZnO solid solution, a prototypical oxide alloy for bandgap engineering. Bandgap predictions can be further improved by introducing non-linearity via gradient-boosted decision trees or neural networks based on the Coulomb matrix descriptor.

SUBMITTER: Midgley SD

PROVIDER: S-EPMC8279729 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case Study.

Midgley Scott D SD Hamad Said S Butler Keith T KT Grau-Crespo Ricardo R

The journal of physical chemistry letters 20210525 21

Computer simulations of alloys' properties often require calculations in a large space of configurations in a supercell of the crystal structure. A common approach is to map density functional theory results into a simplified interaction model using so-called cluster expansions, which are linear on the cluster correlation functions. Alternative descriptors have not been sufficiently explored so far. We show here that a simple descriptor based on the Coulomb matrix eigenspectrum clearly outperfor ...[more]

PMID: 34032426

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