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

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

Content Provider	MDPI
Author	Filipoiu, Nicolae Mitran, Tudor Luca Anghel, Dragos Victor Florea, Mihaela Pintilie, Ioana Manolescu, Andrei Nemnes, George Alexandru
Copyright Year	2021
Description	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.
Starting Page	5431
e-ISSN	19961073
DOI	10.3390/en14175431
Journal	Energies
Issue Number	17
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2021-09-01
Access Restriction	Open
Subject Keyword	Energies Hardware and Architecturee Mixed-cation Mixed-halide Perovskite Optical Absorption Degradation Mechanisms Machine-learning Techniques
Content Type	Text
Resource Type	Article

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