Machine learning the DFT potential energy surface for inorganic halide perovskite CsPbBr${}_3$

Abstract

Structural phase transitions as a function of temperature dictate 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 no\ n-convex 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 deformati\ ons. 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 l\ ow error.

References in corpus (15)

• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Machine Learning Unifies the Modelling of Materials and Molecules
• Entropy driven stabilization of energetically unstable crystal structures explained from first principles theory
• Lattice anharmonicity and thermal conductivity from compressive sensing of first-principles calculations
• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• Spontaneous Octahedral Tilting in the Cubic Inorganic Caesium Halide Perovskites CsSnX${}_3$ and CsPbX${}_3$ (X = F, Cl, Br, I)
• Compressive sensing as a new paradigm for model building
• Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels
• First-principles model potentials for lattice-dynamical studies: general methodology and example of application to ferroic perovskite oxides
• Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals
• Localized Basis for Effective Lattice Hamiltonians: Lattice Wannier Functions
• Free energy and molecular dynamics calculations for the cubic-tetragonal phase transition in zirconia
• Local atomic geometry and Ti 1s near-edge spectra in PbTiO${}_3$ and SrTiO${}_3$

Cited by in corpus (5)

• Deep Learning of Accurate Force Field of Ferroelectric HfO${}_2$
• Phase transitions in inorganic halide perovskites from machine learning potentials
• Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
• Revealing the free energy landscape of halide perovskites: Metastability and transition characters in CsPbBr${}_3$ and MAPbI${}_3$
• Phase stability in the Hf-N and Zr-N systems