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WIREs Comput Mol Sci
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High‐throughput computational materials screening and discovery of optoelectronic semiconductors

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Abstract In the recent past, optoelectronic semiconductors have attracted significant research attention both experimentally and theoretically toward large‐scale applications in energy conversion, lighting, imaging, detection, and so on. With advancement in computing power and rapid development of computational algorithms, scientific community resorts to materials simulation to explore the hidden potential behind thousands of potentially unknown materials within short timeframes that the real experiments might take a long time. Within this context, the high‐throughput (HT) computational materials screening has emerged as a useful tool to accelerate materials discovery, especially in the field of optoelectronic semiconductors. One of the important consequences is the construction of a number of material databases containing wide range of functional materials with their diverse physical properties and applications. Herein, we reviewed the recent progress on HT computational screening of optoelectronic semiconductors, with focus on photovoltaic solar absorbers, photoelectrochemical cells, semiconductor light‐emitting diodes, and transparent conducting materials. We have also summarized the general workflow of HT computational screening, released workhorse models, and existing material databases. Finally, we offer perspectives for future research with a hope that this study could inspire new ideas for computational‐driven optoelectronic semiconductor discovery in the HT routine. This article is categorized under: Structure and Mechanism > Computational Materials Science
The virtual and experimental discovery pipeline for design of molecular organic light‐emitting diodes (OLEDs) (left): The search space was reduced from six orders of magnitude to one. The cubes represent the size of search space at each stage. Initially, the high‐throughput screening combining with machine learning calculations was performed. Finally, several optimum candidates were synthesized and tested experimentally. The density functional theory calculation workflow and details (right): the calculations in the backbone were performed for all considered molecules. The leading compounds were characterized using the methods in emission part. Moreover, the calculations in benchmarking part were used for assessing predictive power. (Reprinted with permission from Reference 149. Copyright 2016 Springer Nature)
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The workflow of screening nontoxic, abundant and low Herfindahl–Hirschman index (HHI) materials from Open Quantum Materials Database (OQMD) as potential photovoltaic or photoelectrochemical device materials. The candidates possess band gap in the range 0 eV < 2 eV, low carrier effective masses and high defect tolerance. (Reprinted with permission from Reference 15 Copyright 2018 American Chemical Society)
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(a) The search space of candidate A2M+M3+XVII6 perovskites based on chemical substitution; left panel depicts double perovskite structure and the right panel shows schematic representation of cation transmutation. (b) The high‐throughput screening process and results. Only 11 compounds with high photovoltaic performance such as high stability (ΔH > 0), suitable band gaps (0.8–2.0 eV), high charge carrier mobility (|m| < 1.0 m0) and small excitons binding energies (ΔEb < 100 meV)) are marked with red checks.(Reprinted with permission from Reference 86. Copyright 2017 American Chemical Society)
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The funnel type model of high‐throughput computational screening
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The structure prototype of optoelectronic octahedral oxyhalides (OOHs) and the workflow of high‐throughput density functional theory (HT‐DFT) and machine learning (ML) approach workflow. (a) Top and side views of a 2*2 unit cell of a conventional OOH structure. (b) The model of distorted stacking octahedrons with A‐site (53 elements) at the center and chalcogens B1/B2 (4 elements), halogens X1/X2 (4 elements) are at the octahedral sites. (c) The left panel shows the prediction model comprising four parts: input data set, ML model, ML prediction, and preliminary screening. The right panel represents the workflow of high‐throughput computational screening and discovery of optoelectronic materials. (Reprinted with permission from Reference 179. Copyright 2019 American Chemical Society)
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The upper panel shows flowchart design for identifying Li‐doped Cr2MnO4 p‐type transparent conducting oxide (TCOs). (a) The workflow of the inverse design approach; (b) the mark table of the screening results. A check mark indicates suitable candidate with the desired functionality for a p‐type TCO. A circle shows not optimum, but still has acceptable value for this property. A cross mark represents the prohibitive property which means the compound is not suitable.(Reprinted with permission from Reference 25. Copyright 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim). The lower part is the generic high‐throughput screening funnel for transparent conductors. The first four filters (stability, transparency, conductivity, dopabilty) can be derived from density functional theory (DFT) calculations, whereas refinements indicate additional screening parameters such as optical properties, band alignment nontoxic, element abundant, and so on. (Reprinted with permission from Reference 170. Copyright 2018 American Chemical Society)
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Schematic design of the high‐throughput screening of light‐emitting diode (LED) and photovoltaic (PV) materials from hypothetical halide semiconductors. Compounds with direct band gaps in the visible spectrum range are ideal for LED, and candidates with band gaps in the range from 0.8 to 2.2 eV are identified as solar absorber materials. (Reprinted with permission from Reference 24. Copyright 2019 Royal Society of Chemistry)
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