Discovering Novel Halide Perovskite Alloys using Multi-Fidelity Machine Learning and Genetic Algorithm

TL;DR

This study uses multi-fidelity machine learning models trained on DFT and experimental data to efficiently discover novel halide perovskite alloys with desirable optoelectronic properties through inverse design and genetic algorithms.

Contribution

The paper introduces a multi-fidelity machine learning framework combined with genetic algorithms for inverse design of stable, efficient halide perovskite alloys, expanding the materials discovery toolkit.

Findings

Identified thousands of promising perovskite materials with low decomposition energy and suitable band gaps.

Developed surrogate models capable of predicting properties for over 150,000 hypothetical compounds.

Discovered hundreds of optimal compositions with high photovoltaic efficiency.

Abstract

Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a high-throughput density functional theory (DFT) dataset of halide perovskite alloys can be used to train accurate surrogate models for property prediction and subsequently perform inverse design using genetic algorithm (GA). Our dataset consists of decomposition energies, band gaps, and photovoltaic efficiencies of nearly 800 pure and mixed composition ABX$_3$ compounds from both the GGA-PBE and HSE06 functionals, and are combined with ~ 100 experimental data points collected from the literature. Multi-fidelity random forest regression models are trained on the DFT + experimental dataset for each property using descriptors that one-hot encode composition, phase, and fidelity, and additionally include well-known elemental or molecular properties of species at the A, B, and X sites. Rigorously optimized models are deployed for experiment-level prediction over > 150,000 hypothetical compounds, leading to thousands of promising materials with low decomposition energy, band gap between 1 and 2 eV, and efficiency > 15%. Surrogate models are further combined with GA using an objective function to maintain chemical feasibility, minimize decomposition energy, maximize PV efficiency, and keep band gap between 1 and 2 eV; hundreds more optimal compositions and phases are thus discovered. We present an analysis of the screened and inverse-designed materials, visualize ternary phase diagrams generated for many systems of interest using ML predictions, and suggest strategies for further improvement and expansion in the future.