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Exploring New Approaches towards the Formability of Mixed-Ion Perovskite by DFT and Machine Learning

Abstract

Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers. These have led to the successful enhancement of their stability, a feature that is often counterbalanced by a reduction of their power-conversion efficiency. In order to provide a systematic analysis of the structure-to-property relations of this class of compounds we have performed highthroughput density functional theory calculations exploring fully inorganic ABC3 chalcogenide (I-V-VI3), halide (I-II-VII3) and hybrid perovskites. Special attention has been given to structures featuring three-dimensional BC6 octahedral networks because of their efficient carrier transport properties. In particular we have carefully analyzed therole of BC6 octahedral deformations, rotations and tilts over the thermodynamic stability and optical properties of the compounds. By using machine learning algorithms we have estimated the relations between the octahedral deformation and the bandgap, and established a similarity map among all the calculated compounds.

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Publication details

The article was received on 11 Nov 2018, accepted on 07 Dec 2018 and first published on 07 Dec 2018


Article type: Paper
DOI: 10.1039/C8CP06528D
Citation: Phys. Chem. Chem. Phys., 2018, Accepted Manuscript
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    Exploring New Approaches towards the Formability of Mixed-Ion Perovskite by DFT and Machine Learning

    H. Park, R. Mall, F. H. Alharbi, S. Sanvito, N. Tabet, H. Bensmail and F. El-Mellouhi, Phys. Chem. Chem. Phys., 2018, Accepted Manuscript , DOI: 10.1039/C8CP06528D

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