Machine learning the density functional theory potential energy surface for the inorganic halide perovskite CsPbBr3

The temperature and pressure dependence of structural phase transitions determine the structure-functionality relationships in many technologically important materials. Harmonic Hamiltonians have proven successful in predicting the vibrational properties of many materials. However, they are inadequa...

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Bibliographic Details
Published in:Physical review. B 2019-10, Vol.100 (13)
Main Authors: Thomas, John C, Bechtel, Jonathon S, Natarajan, Anirudh Raju, Van der Ven, Anton
Format: Article
Language:English
Subjects:
Anharmonicity
Artificial intelligence
Clusters
Crystal structure
Density functional theory
First principles
Machine learning
Perovskites
Phase transitions
Polynomials
Potential energy
Pressure dependence
Temperature dependence
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Summary:The temperature and pressure dependence of structural phase transitions determine the structure-functionality relationships in many technologically important materials. Harmonic Hamiltonians have proven successful in predicting the vibrational properties of many materials. However, they are inadequate for modeling structural phase transitions in crystals with potential energy surfaces that are either strongly anharmonic or nonconvex with respect to collective atomic displacements or homogeneous strains. In this paper we develop a framework to express highly anharmonic first-principles potential energy surfaces as polynomials of collective cluster deformations. We further adapt the approach to a nonlinear extension of the cluster expansion formalism through the use of an artificial neural net model. The machine learning models are trained on a large database of first-principles calculations and are shown to reproduce the potential energy surface with low error.
ISSN:2469-9950
2469-9969
DOI:10.1103/PhysRevB.100.134101