Investigation of Opto-Electronic Properties and Stability of Mixed-Cation Mixed-Halide Perovskite Materials with Machine-Learning Implementation

Nicolae Filipoiu, Tudor Luca Mitran, Dragos Victor Anghel, Mihaela Florea, Ioana Pintilie, Andrei Manolescu and George Alexandru Nemnes
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Nicolae Filipoiu: Horia Hulubei National Institute for Physics and Nuclear Engineering, 077126 Magurele, Ilfov, Romania
Tudor Luca Mitran: Horia Hulubei National Institute for Physics and Nuclear Engineering, 077126 Magurele, Ilfov, Romania
Dragos Victor Anghel: Horia Hulubei National Institute for Physics and Nuclear Engineering, 077126 Magurele, Ilfov, Romania
Mihaela Florea: National Institute of Materials Physics, 077125 Magurele, Ilfov, Romania
Ioana Pintilie: National Institute of Materials Physics, 077125 Magurele, Ilfov, Romania
Andrei Manolescu: Department of Engineering, Reykjavik University, Menntavegur 1, IS-102 Reykjavik, Iceland
George Alexandru Nemnes: Horia Hulubei National Institute for Physics and Nuclear Engineering, 077126 Magurele, Ilfov, Romania

Energies, 2021, vol. 14, issue 17, 1-19

Abstract: The feasibility of mixed-cation mixed-halogen perovskites of formula A x A’ 1 − x PbX y X’ z X” 3 − y − z is analyzed from the perspective of structural stability, opto-electronic properties and possible degradation mechanisms. Using density functional theory (DFT) calculations aided by machine-learning (ML) methods, the structurally stable compositions are further evaluated for the highest absorption and optimal stability. Here, the role of the halogen mixtures is demonstrated in tuning the contrasting trends of optical absorption and stability. Similarly, binary organic cation mixtures are found to significantly influence the degradation, while they have a lesser, but still visible effect on the opto-electronic properties. The combined framework of high-throughput calculations and ML techniques such as the linear regression methods, random forests and artificial neural networks offers the necessary grounds for an efficient exploration of multi-dimensional compositional spaces.

Keywords: mixed-cation; mixed-halide; perovskite; optical absorption; degradation mechanisms; machine-learning techniques (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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