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RESEARCH PAPER · Wiley Interdisciplinary Reviews Computational Molecular Science · 2020

High‐throughput computational materials screening and discovery of optoelectronic semiconductors

Shulin Luo, Tianshu Li, Xinjiang Wang, Muhammad Faizan, Lijun Zhang

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High‐throughput computational materials screening and discovery of optoelectronic semiconductorsOriginalHigh-throughput design of magnetic materials52Efficiency Models for GaN-Based Light-Emitting Diodes: Status and Challenges62Theoretical prediction of solution in ScxY1–x Fe2 and order-disorder transitions in V2x Fe2(1–x)Zr1Discovery of Lead-Free Hybrid Organic/Inorganic Perovskites Using Metaheuristic-Driven DFT Calculations30Simulation-based machine learning for optoelectronic device design: perspectives, problems, and prospects25Discovery of New Phases of Bismuth Oxyselenide Semiconductor Bi2OSe2 by Global Structure Search Approach2Machine Learning in Magnetic Materials48Phase transition pathway of hybrid halide perovskites under compression: Insights from first-principles calculations16High‐throughput computational materials screening and discovery of optoelectronic semiconductors11JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics85Computational discovery of energy materials in the era of big data and machine learning: A critical review60The Advantage of Nanowire Configuration in Band Structure Determination9Rotation Equivariant Fourier Neural Operators for Learning Symmetry Preserving Transformations on Scalar Fields, Vector Fields, and Higher Order Tensor Fields0Integrated machine learning-quantitative structure property relationship (ML-QSPR) and chemical kinetics for high throughput fuel screening toward internal combustion engine28Metal Halide Semiconductors beyond Lead-Based Perovskites for Promising Optoelectronic Applications54Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning87Theory-oriented screening and discovery of advanced energy transformation materials in electrocatalysis406Data-Driven Design of Nanopore Graphene for Water Desalination23Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis69Global instability index as a crystallographic stability descriptor of halide and chalcogenide perovskites43Robust Design of High-Performance Optoelectronic Chalcogenide Crystals from High-Throughput Computation60A robust and efficient line search for self-consistent field iterations7Evaluation of performance of machine learning methods in mining structure–property data of halide perovskite materials21Unsupervised machine learning to classify crystal structures according to their structural distortion: A case study on Li-argyrodite solid-state electrolytes11Discovery of direct band gap perovskites for light harvesting by using machine learning59Design principles of hydrogen-evolution-suppressing single-atom catalysts for aqueous electrosynthesis44Inorganic Crystal Structure Prototype Database Based on Unsupervised Learning of Local Atomic Environments11Generative design of stable semiconductor materials using deep learning and density functional theory24Designing limiting-efficiency BaSi 2 solar cells by device simulation and computational material screening26Design of New Ternary Nitrides for Photovoltaic Applications via High-Throughput Calculations8Indirect Band Gap Semiconductors for Thin-Film Photovoltaics: High-Throughput Calculation of Phonon-Assisted Absorption74Challenges and Opportunities of Chemiresistors Based on Microelectromechanical Systems for Chemical Olfaction52Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis82Emerging Chalcohalide Materials for Energy Applications163Federated data processing and learning for collaboration in the physical sciences11Accelerate the Design of New Superhard Carbon Allotropes in the Pca21 Space Group: High-Throughput Screening and Machine Learning Strategies1The Challenges of Integrating the Principles of Green Chemistry and Green Engineering to Heterogeneous Photocatalysis to Treat Water and Produce Green H229Thermal decomposition study of cadmium telluride (CdTe)28Small data machine learning in materials science864Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials102High-throughput calculations of charged point defect properties with semi-local density functional theory—performance benchmarks for materials screening applications56A computational study of ZnFeX2 (X = S, Se, Te) Nano-clusters having photovoltaic applications4Automatic Multiscale Method of Building up a Cross-linked Polymer Reaction System: Bridging SMILES to the Multiscale Molecular Dynamics Simulation8Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows20From the perspective of experimental practice: High-throughput computational screening in photocatalysis15High-Throughput Screening of Hole Transport Materials for Quantum Dot Light-Emitting Diodes22Rational design of quantum spin Hall phase in type-III van der Waals heterostructures6High-Throughput Computational Screening of Experimental Zr-Based MOFs for Elemental Mercury Capture8Investigation of heavy thallium perovskites TlGeX (X = Cl, Br and I) for optoelectronic and thermoelectric applications: A DFT study84Analysis of good and bad fingerprint for identification of NIR based optical frameworks using Monte Carlo method4Identifying Stable Electrocatalysts Initialized by Data Mining: Sb 2 WO 6 for Oxygen Reduction26Data Lineage and Scientific Traceability in Computational Materials Pipelines0Photoelectrochemical Water Splitting0New Layered II–III–VI Semiconductors: Promising Materials for High Performance Solar Cells17Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age8Revolutionizing physics: a comprehensive survey of machine learning applications25Automated analysis of surface facets: the example of cesium telluride6Methods and applications of machine learning in computational design of optoelectronic semiconductors22Roadmap on printable electronic materials for next-generation sensors61Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies33Predicting the thermoelectric figure of merit in p-type BiSbTe-based alloys using artificial neural network modeling4Explorative prediction of novel superhard carbon allotropes with lager cell: Density functional theory-assisted deep learning10A differentiable quantum phase estimation algorithm3DFT/TD-DFT study of novel triphenylamine-based dyes with azo moieties and π-spacer variations for enhanced dye-sensitized solar cell performance16Crystal Structure Prediction Using Generative Adversarial Network with Data-Driven Latent Space Fusion Strategy19Construction and application of materials database under data-driven research paradigm3The devil in the details: lessons from Li 6 PS 5 X for robust high-throughput workflows3Atomic-level environment engineering in carbon-based single-atom catalysts: a review of theoretical insights for hydrogen evolution and triiodide reduction3Cascading Risk in Autonomous Materials Design: Governance Failure Propagation0AB-initio study of electronic, elastic, and optical properties of double antiperovskite, L i 6 A g B i X 2 (X = Cl, Br, I)4Luminescence From Localized States in Solids: A First‐Principles Perspective27Unveiling the potential of Ca₃PBr₃ perovskite for solar applications: A study of structural, mechanical, electronic and optical properties3Intelligent nanophotonics: when machine learning sheds light76ByteQC : GPU ‐Accelerated Quantum Chemistry Package for Large‐Scale Systems7Enhancing Photocurrent of Bismuth Manganate Photoanodes with Nanoislands of Bismuth Oxide1Accelerated data-driven materials science with the Materials Project296A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties5Artificial Intelligence for Materials Discovery, Development, and Optimization170Designing new antiperovskites derivatives Ra 3 BiA 3 ′ (A ′ = F, Cl, Br, I) for photovoltaic application: A DFT study1Rational design of triphenylamine-based sensitizers for DSSCs: a DFT comparison of pyridine and amine donor substituents0Materials for thermochemical energy storage and conversion: attributes for low-temperature applications15Bandgap prediction and design of halide double perovskites via ensemble machine learning6TMDCs and related vdW-heterostructures: a gap analysis of hybrid synthesis and the realization of optimal properties for practical applications, current challenges, and future prospects4High-Throughput Computational Framework for the Discovery of Structurally Diverse Monomers in High-Strength Aramid Fibers1Two-timescale-based hybrid modeling framework for crystallization processes3Plant-Based Nanotechnology for Proteomic and Genomic: Current Trends, Approaches and Applications2Artificial intelligence-driven approaches for materials design and discovery102Computational material screening for electrode materials of BaSi 2 solar cells0Computation-assisted probing of the solid-solid interface in all-solid-state sodium-ion batteries1Data-driven discovery of high-performance quaternary chalcogenide photovoltaics1Revolutionizing layered double hydroxides for adsorption, catalysis and energy storage through artificial intelligence and machine learning: A critical review3Structural, electronic, mechanical, and optical properties of SnSiO3 and PbSiO3 Perovskites: A first-principles insight into silicon-based oxide optoelectronic materials1Identifying A/B-Site Effects and Screening of β-ABO 2 Ferroelectrics toward Optoelectronic Functionality0Modelling and Simulations of Perovskite Solar Cells0Unveiling the auxiliary acceptor role of azo spacers in triphenylamine-based sensitizers: A computational insight into spectral broadening and charge transfer mechanisms0SemiConLens: Visual Analytics for 2D Semiconductor Discovery0High-throughput screening of MN₂/XYAZ₂ van der Waals heterostructures: Controllable Schottky-to-Ohmic contact conversion via electric field modulation0DynStabNet: A Deep Learning Framework for Fast Dynamical Stability Prediction of Crystal Structures0A review of transition metal-based double perovskite oxides for photovoltaic applications0Predicting the electrical conductivity of doped TiO2 via machine learning with special attention enhancement on first principles featuring data0

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