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Bulk and Surface DFT investigations of Inorganic Halide Perovskites Screened Using Machine Learning and Materials Property Databases


In the recent past, there has been a proliferation in high-throughput density functional theory and data-driven explorations of materials motivated by a need to reduce physical testing and costly computations for materials discovery. This has, in conjunction with the development of open-access materials property databases, encouraged accelerated and more streamlined discovery and screening of technologically relevant materials. In this work, we report our results on the screening and DFT studies of one such class of materials, i.e. ABX3 inorganic halide perovskites (A, B and X representing the monovalent, divalent and halide ions respectively) using a coupled machine-learning (ML) and density functional theory (DFT) approach. Utilizing the Support Vector Machine algorithm, we predict the formability of 454 inorganic halide compounds in the perovskite phase. The compounds with a formation probability P >= 0.8 are further checked for thermodynamic stability in at least one of these three open materials databases – Materials Project (MP), Automatic FLOW for Materials Discovery (AFLOW) and Open Quantum Materials Database (OQMD). The shortlisted candidate perovskites are then considered for DFT computations. Taking input geometries from MP’s structure predictor, the optimized lattice parameters and computed band gaps (BG) for all screened compounds are compared with predictions across all databases. Subsequently, detailed studies on low index surfaces are presented for two halide perovksites – RbSnCl3 and RbSnBr3 – having band-gaps in favourable range for photovoltaics (PV). Different possible (100), (110) and (111) surface terminations are investigated for each of these compositions and the atomic relaxations, surface energies and electronic band structure are reported for each termination. To the best of our knowledge, no surface studies have been reported in literature for any of the halide perovskites present in our database. These studies, therefore, are an important step towards gaining a fundamental understanding of the interfacial properties of perovskites, which can help facilitate further breakthroughs in the PV technology.

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Publication details

The article was received on 08 Jun 2019, accepted on 09 Aug 2019 and first published on 09 Aug 2019

Article type: Paper
DOI: 10.1039/C9CP03240A
Phys. Chem. Chem. Phys., 2019, Accepted Manuscript

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    Bulk and Surface DFT investigations of Inorganic Halide Perovskites Screened Using Machine Learning and Materials Property Databases

    D. Jain, S. Chaube, P. P. Khullar, S. Goverapet Srinivasan and B. Rai, Phys. Chem. Chem. Phys., 2019, Accepted Manuscript , DOI: 10.1039/C9CP03240A

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