Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

• Published in: Journal of materials science Vol. 57; no. 23; pp. 10736 - 10754
• Main Authors: Mannodi-Kanakkithodi, Arun, Chan, Maria K. Y.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2022; Springer; Springer Nature B.V
• Subjects: Analysis; behavior; Characterization and Evaluation of Materials; Chemistry and Materials Science; Classical Mechanics; Computation; Computational Materials Design; Crystallography and Scattering Methods; Current carriers; Density functional theory; Density functionals; energy; Free energy; Gaussian process; Heat of formation; Impurities; lead; Lead compounds; Machine learning; MATERIALS SCIENCE; Metal halides; Neural networks; optoelectronic properties; Optoelectronics; Perovskite; Perovskites; phase; Photovoltaic cells; photovoltaics; Point defects; Polymer Sciences; Prediction models; Predictions; Screening; Solar cells; Solid Mechanics
• ISSN: 0022-2461; 1573-4803
• DOI: 10.1007/s10853-022-06998-z

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide ( MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. This accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.