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

Mannodi-Kanakkithodi, Arun; Chan, Maria K. Y.

doi:10.1007/s10853-022-06998-z

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

Journal Article · Fri Mar 04 00:00:00 EST 2022 · Journal of Materials Science

DOI:https://doi.org/10.1007/s10853-022-06998-z· OSTI ID:1898519

^[1]; Chan, Maria K. Y. ^[2]

Purdue Univ., West Lafayette, IN (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)

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 (MAPbX₃) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX₃ 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 MAPbX₃ 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. Finally, 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: Purdue University; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division

Grant/Contract Number:: AC02-05CH11231; AC02-06CH11357

OSTI ID:: 1898519

Journal Information:: Journal of Materials Science, Journal Name: Journal of Materials Science Journal Issue: 23 Vol. 57; ISSN 0022-2461

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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