DeePKS Model for Halide Perovskites with the Accuracy of Hybrid Functional

TL;DR

This paper introduces a neural network-based DeePKS model that predicts electronic properties of halide perovskites with hybrid functional accuracy and GGA-level efficiency, aiding solar cell material design.

Contribution

The study develops a universal DeePKS model trained to replicate hybrid functional results with high accuracy and efficiency for diverse halide perovskites.

Findings

DeePKS accurately predicts band gaps, forces, and DOS in agreement with HSE06.

Model achieves hybrid functional accuracy with GGA-level computational efficiency.

DeePKS+SOC provides consistent results for Pb-containing perovskites without SOC training.

Abstract

Accurate prediction for the electronic structure properties of halide perovskites plays a significant role in the design of highly efficient and stable solar cells. While density functional theory (DFT) within the generalized gradient approximation (GGA) offers reliable prediction in terms of lattice constants and potential energy surface for halide perovskites, it severely underestimates the band gap due to the lack of non-local exact exchange term, which exists in computationally expensive hybrid functionals. In this work, a universal Deep Kohn-Sham (DeePKS) model based on neural network is trained so as to enable electronic structure calculations with the accuracy of hybrid functional HSE06 and the efficiency comparable to GGA functional, for a plethora of halide perovskites, i.e., ABX$_3$ (A=FA, MA, Cs; B=Sn, Pb; X=Cl, Br, I). Forces, band gaps, and density of states (DOS) predicted by our DeePKS model for all aforementioned perovskites are in good agreement with the HSE06 results, with significantly improved efficiency. In addition, even though the spin-orbit coupling (SOC) effect has not been taken into consideration during the training process, DeePKS+SOC offers highly consistent band gap and DOS as compared to HSE06+SOC for Pb-containing systems. We believe such DeePKS model can be readily applied for an accurate yet efficient prediction of various properties for the family of halide perovskites.