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

## TL;DR

This paper introduces a machine learning framework that models highly anharmonic potential energy surfaces of inorganic halide perovskites, enabling accurate predictions of structural phase transitions beyond harmonic approximations.

## Contribution

It develops a novel neural network-based approach to express complex anharmonic potential energy surfaces as polynomial cluster expansions, trained on first-principles data.

## Key findings

- Accurately reproduces the potential energy surface with low error.
- Enables modeling of structural phase transitions in complex materials.
- Extends cluster expansion formalism with machine learning techniques.

## 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.