Data-driven design of novel halide perovskite alloys

Mannodi-Kanakkithodi, Arun; Chan, Maria Y.

doi:10.1039/d1ee02971a

Data-driven design of novel halide perovskite alloys

Journal Article · Mon Mar 21 00:00:00 EDT 2022 · Energy & Environmental Science

DOI:https://doi.org/10.1039/d1ee02971a· OSTI ID:1908117

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

The great tunability of the properties of halide perovskites presents new opportunities for optoelectronic applications as well as significant challenges associated with exploring combinatorial chemical spaces. Here, in this work, we develop a framework powered by high-throughput computations and machine learning for the design and prediction of mixed cation halide perovskite alloys. In a chemical space of ABX₃ perovskites with a selected set of options for A, B, and X species, pseudo-cubic structures with B-site mixing are simulated using density functional theory (DFT) and several properties are computed, including stability, lattice constant, band gap, vacancy formation energy, refractive index, and optical absorption spectrum, using both semi-local and hybrid functionals. Neural networks (NN) are used to train predictive models for every property using tabulated elemental properties of A, B, and X site species as descriptors. Starting from a DFT dataset of 229 points, we use the trained NN models to predict the structural, energetic, electronic, and optical properties of an extensive dataset of 17955 compounds, and perform high-throughput screening in terms of stability, band gap, and defect tolerance, to obtain 392 promising compounds that are ranked as potential absorbers according to their photovoltaic figure of merit. Compositional trends in the screened set of attractive mixed cation halide perovskites are revealed and additional computations are performed on selected compounds. The data-driven design framework developed here is promising for designing novel mixed compositions and can be extended to a wider perovskite chemical space in terms of A, B, and X species, different kinds of mixing at the A, B, or X sites, non-cubic phases, and other properties of interest.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

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

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

OSTI ID:: 1908117

Alternate ID(s):: OSTI ID: 1860277

Journal Information:: Energy & Environmental Science, Journal Name: Energy & Environmental Science Journal Issue: 5 Vol. 15; ISSN 1754-5692

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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