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RESEARCH PAPER · Nano Energy · 2020

Machine learning for halide perovskite materials

Lei Zhang, Mu He, Shaofeng Shao

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Machine learning for halide perovskite materialsOriginalNanostructured and Heterostructured 2D Materials for Thermoelectrics50Accelerated discovery of stable spinels in energy systems via machine learning56Chemical formula input relied intelligent identification of an inorganic perovskite for solar thermochemical hydrogen production4Machine Learning Accelerated Insights of Perovskite Materials3Machine learning for advanced energy materials208A machine learning based optimization method towards removing undesired deformation of energy-absorbing structures55Cosensitization-based halide perovskite in aqueous solution: A photoelectrochemical and first-principles investigation8Global Property Prediction: A Benchmark Study on Open-Source, Perovskite-like Datasets14Machine learning and symbolic regression investigation on stability of MXene materials83Studies on the regularity of perovskite formation via machine learning35Is machine learning redefining the perovskite solar cells?59Machine Learning for Sustainable Energy Systems109Relationships between Distortions of Inorganic Framework and Band Gap of Layered Hybrid Halide Perovskites68Machine Learning Accelerates the Discovery of Light-Absorbing Materials for Double Perovskite Solar Cells28Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells90Moisture-stimulated reversible thermochromic CsPbI3-xBrx films: In-situ spectroscopic-resolved structure and optical properties20Bismuth-based halide perovskite and perovskite-inspired light absorbing materials for photovoltaics59A General View of Big Data and Machine Learning1A machine vision tool for facilitating the optimization of large-area perovskite photovoltaics32Prediction of solar cell materials via unsupervised literature learning11Accelerated discovery of defect tolerant organo-halide perovskites1Intelligent control of nanoparticle synthesis through machine learning83Prediction of Wind Load Power Spectrum on High-Rise Buildings by Machine Learning Based Algorithms0Efficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning30Determination of the fatigue equation for the cement-stabilized cold recycled mixtures with road construction waste materials based on data-driven41Bond length alternation- and data mining-assisted exploration of molecular adsorbates with π-conjugation and amines for two-dimensional halide perovskite surface2Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm707Interpretable Machine Learning for Investigating Photoelectrochemical Properties of Cosensitizer-Based CH3NH3PbI3/TiO2 Films in Water11Strategies for high‐performance perovskite solar cells from materials, film engineering to carrier dynamics and photon management56Electron transfer dominated triboelectrification at the hydrophobic/slippery substrate—water interfaces18Analysis of structural, elastic and optoelectronic properties of indium-based halide perovskites InACl3 (A = Ge, Sn, Pb) using density functional theory53The effects of organic cation rotation in hybrid Organic-Inorganic Perovskites: A critical review48Deep Learning for Materials Recognition from FTIR Images0Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning102Machine‐Learning Modeling for Ultra‐Stable High‐Efficiency Perovskite Solar Cells86Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses11Red Perovskite Light‐Emitting Diodes: Recent Advances and Perspectives72Investigation of photovoltaic performance of lead-free CsSnI3-based perovskite solar cell with different hole transport layers: First Principle Calculations and SCAPS-1D Analysis194Crystal structural prediction of perovskite materials using machine learning: A comparative study20A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A 2 B + B 3+ X 615Machine learning in energy chemistry: introduction, challenges and perspectives31Perovskite-Based Materials for Photovoltaic Applications: A Machine Learning Approach0Perovskite-quantum dot hybrid solar cells: a multi-win strategy for high performance and stability17Process Insights into Perovskite Thin‐Film Photovoltaics from Machine Learning with In Situ Luminescence Data27High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap35Machine Learning for Halide Perovskite Materials ABX3 (B = Pb, X = I, Br, Cl) Assessment of Structural Properties and Band Gap Engineering for Solar Energy46Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives241Machine learning for perovskite solar cell design58First-principles and machine learning modeling on adsorption of atmospheric gases on two-dimensional Ruddlesden–Popper halide perovskite surface7Electrochemical direct CO2 capture technology using redox-active organic molecules to achieve carbon-neutrality56The role of machine learning in perovskite solar cell research42First-principles, machine learning and symbolic regression modelling for organic molecule adsorption on two-dimensional CaO surface12Center-environment deep transfer machine learning across crystal structures: from spinel oxides to perovskite oxides60Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods5High-Throughput Screening on Halide Perovskite Derivatives and Rational Design of Cs 3 LuCl 629Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Application of Machine Learning in Material Synthesis and Property Prediction166Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76From prediction to design: Recent advances in machine learning for the study of 2D materials90Rotor lattice model of ferroelectric large polarons4Prediction of photoelectric conversion efficiency of organic photovoltaic materials based on deep learning1Absorber Layer Thickness as a New Feature in Statistical Learning Tools of Perovskite Solar Cells5Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization3Enhanced optical and energy storage properties of K0.5Na0.5NbO3 lead-free ceramics by doping Bi(Sr0.5Zr0.5)O335Accelerated design of stable halide perovskite heterostructure film in hostile condition via surface modifier5Comprehensive and Accurate Prediction of Band Gap for Lead-Free Double Perovskites through Self-Modified Machine Learning Strategy10Machine learning–assisted colloidal synthesis: A review39A machine learning-assisted study of the formation of oxygen vacancies in anatase titanium dioxide9Development and application of Few-shot learning methods in materials science under data scarcity47Nature-inspired and computer-aided approaches to enable improved photovoltaic materials, more efficient processing, and novel devices0High-Throughput Ensemble-Learning-Driven Band Gap Prediction of Double Perovskites Solar Cells Absorber30Coordination of Thermally Activated Delayed Fluorescent Molecules for Efficient and Stable Perovskite Light‐Emitting Diodes13Deciphering N-Doped Biochar Design for Non-Radical Pathways through Hierarchical Machine Learning23Accelerating the discovery of direct bandgap doped-spinel photovoltaic materials: A target-driven approach using interpretable machine learning7B-site deficient hexagonal perovskites: Structural stability, ionic order-disorder and electrical properties36Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials21Predicting photovoltaic parameters of perovskite solar cells using machine learning9Employing the Interpretable Ensemble Learning Approach to Predict the Bandgaps of the Halide Perovskites12Accelerated Design for Perovskite-Oxide-Based Photocatalysts Using Machine Learning Techniques18Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles8Unveiling Feature Importance in Methanol Reforming Systems through the Machine Learning4Improving thermodynamic stability of double perovskites with machine learning: The role of cation composition28Prediction of wind load power spectrum on high-rise buildings by various machine learning algorithms12Adapting Explainable Machine Learning to Study Mechanical Properties of 2D Hybrid Halide Perovskites16Predicting ammonia emissions and global warming potential in composting by machine learning35Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data6Navigating the complexity of hybrid materials without structural dependency: PerovGNN as a map2A SYSTEMATIC LITERATURE REVIEW ON ENERGY-EFFICIENT TRANSFORMER DESIGN FOR SMART GRIDS0Accelerating materials property discovery in uncharted domains through the integration of high-throughput computation and machine learning2Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication36Innovative computational framework for Sr 3 SbCl 3 absorber optimization: DFT, SCAPS-1D, and machine learning perspectives28Quantum Circuit Learning for Predicting Nature of Band Gap of Perovskite Oxides3ESR Study of Double-Exchange Phenomenon in Non-Stoichiometric LaMnO3+δ Correlated to the Crystal Structure11A fast composition-stability machine learning model for screening MAX phases and guiding discovery of Ti 2 SnN17Discovering key factors determining perovskite bandgap under data scarcity inspired by knowledge distillation4Machine Learning-Assisted Optical Characterization and Growth Modulation of Two-Dimensional Materials3Unlocking a self-driving research workflow for perovskite photovoltaics12DFT analysis of Ba2XTaO6 (X = Sm, Er) perovskites: Stability & functional properties19Machine learning for predictive design and optimization of high-performance thermoelectric materials: a review13Advancing perovskite light-emitting diodes: Multifunctional role of polymer, organic, and inorganic ligands in stability and efficiency enhancement10Formability of ABS3 Perovskites1MatPC: Prompting Large Language Model, Crystal Structure Prediction, and First-Principles for Semantic-Driven Material Design3Machine Learning‐Driven Perovskite Research from Experimental Exploration to Industrial Development6Machine learning in X-ray diffraction for materials discovery and characterization19Machine learning-assisted shape prediction for two-dimensional transition metal dichalcogenides growth1Numerical Simulation and Machine Learning-Driven Engineering of K 2 GeI 6 Perovskite Solar Cells for High-Efficiency and Sustainable Photovoltaics27Machine learning facilitates the development of interconnecting layers for perovskite/silicon heterojunction tandem solar cells with proof-of-concept efficiency > 38%0Recent progress on multi-scale calculation of perovskite-based proton-conducting electrolytes for proton ceramic fuel cells7Enhanced quantum support vector regression with quantum kernels and virtual sampling for ABX3 perovskite formation energy2Large Language Models (LLMs) for Materials Design14Chemical diversity encoded in symmetry: universal distortion rules and design principles for perovskites1High throughput generation of high- zT thermoelectrics with precise stoichiometric controls1Unraveling amine–pore interactions regulating CO 2 adsorption–desorption behaviors in amine sorbents0Data-driven assisted design of highly stable and superhard high-entropy diborides2Eco-friendly Cs2SnGeCl6 perovskite absorber: A combined numerical simulation and machine learning analysis for high efficiency solar cells14Extracting Knowledge of 2D‐Hybrid Halide Perovskite Materials: A Data Mining Approach0Efficient Discovery of Lead-Free A 3 BX 6 Halide Perovskites via Machine Learning2Data-driven optimization of lipase-catalyzed butyl butyrate synthesis using active learning and metaheuristic algorithms0Achieving excellent energy storage performance in lead-free K₀.₅Na₀.₅NbO₃-based transparent ferroelectric ceramics6Machine Learning‐Assisted Screening of Small Molecules for Surface Modification of 2D Perovskites0A machine learning framework for screening and band gap prediction of stable cubic halide perovskites for energy applications3Revealing the performance potential of butylammonium-containing 2D iodide perovskite for high-efficiency solar cells: a numerical simulation and machine learning study1Curated Data Set, Structure–Property Relationships, and Machine Learning Prediction of Band Gaps for One-Dimensional Bismuth(III) and Antimony(III) Halides0Thermo-Optical energy properties of Soft-Lattice halide perovskites BaNaI3 and BaKI30Machine Learning Meets Photocatalytic Water Splitting for Sustainable Hydrogen Production0The Critical Role of Tilting Descriptors in Data‐Driven Property Prediction of Orthorhombic Perovskites0Direct band gap lead-free double perovskite semiconductors Rb2AgB’F6 (B’ = In, Ir) for efficient solar cells: Ab-initio study0Integration of SCAPS-1D and Machine Learning for Optimizing CuAgBi2I8 Solar Cells by Employing Copper-Based Chalcogenide Hole Transport Layers0SHAP-augmented interpretable machine learning unlocks lead-free double perovskites for high-performance green light-emitting diodes0

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