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RESEARCH PAPER · Advanced Energy Materials · 2022

Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning Perspectives

Chang Woo Myung, Amir Hajibabaei, Ji‐Hyun Cha, Miran Ha, Junu Kim, Kwang S. Kim

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Challenges, Opportunities, and Prospects in Metal Halide Perovskites from Theoretical and Machine Learning PerspectivesOriginalMachine Learning Aided Interpretable Approach for Single Nucleotide-Based DNA Sequencing using a Model Nanopore15SnO2/BaSnO3 electron transport materials for stable and efficient perovskite solar cells21Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials51Machine learning in energy chemistry: introduction, challenges and perspectives31Machine learning the vibrational free energy of perovskites4Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170The challenge of studying perovskite solar cells’ stability with machine learning28A review on computational modeling of instability and degradation issues of halide perovskite photovoltaic materials24Emerging opportunities for hybrid perovskite solar cells using machine learning8High-Throughput Screening on Halide Perovskite Derivatives and Rational Design of Cs 3 LuCl 629Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12Rotor lattice model of ferroelectric large polarons4Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing22Single‐Atom Heterogeneous Catalysts: Human‐ and AI‐Driven Platform for Augmented Designs, Analytics and Reality‐Enabled Manufacturing1Methylammonium-free wide-bandgap metal halide perovskites for tandem photovoltaics115Benchmark thermodynamic analysis of methylammonium lead iodide decomposition from first principles2A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods25Active sparse Bayesian committee machine potential for isothermal–isobaric molecular dynamics simulations13Screening of novel halide perovskites for photocatalytic water splitting using multi-fidelity machine learning31Perovskite Multiple Quantum Well Superlattices: Potentials and Challenges25Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age8Progress in Single/Multi Atoms and 2D‐Nanomaterials for Electro/Photocatalytic Nitrogen Reduction: Experimental, Computational and Machine Leaning Developments88Research progress of metal halide perovskites in the preparation of nanosemiconductor lasers3An overview about neural networks potentials in molecular dynamics simulation42Data-driven optimization and machine learning analysis of compatible molecules for halide perovskite material22Unprecedented Richness of Temperature‐ and Pressure‐Induced Polymorphism in 1D Lead Iodide Perovskite10Adapting Explainable Machine Learning to Study Mechanical Properties of 2D Hybrid Halide Perovskites16Interpretable Machine Learning for Investigating the Molecular Mechanisms Governing the Transparency of Colorless Transparent Polyimide for OLED Cover Windows33Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices37Sparse Gaussian process based machine learning first principles potentials for materials simulations: Application to batteries, solar cells, catalysts, and macromolecular systems30Machine learning-assisted design of high-performance perovskite photodetectors: a review22Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication36Machine learning for predicting and improving perovskite properties0State of the Art and Prospects for Lead-Free Halide Perovskites in Photocatalytic CO2 Reduction0Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach9A sparse Bayesian Committee Machine potential for oxygen-containing organic compounds9Unlocking a self-driving research workflow for perovskite photovoltaics12Dual DRT-based deconvolution of electrochemical signatures in perovskite solar cells19Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields13Review: machine learning-driven design and discovery of halide perovskite materials1Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44Accelerating Materials Discovery Through Sparse Gaussian Process Machine Learning Potentials6Eu 3+ -induced passivation and charge-transport modulation in Cs 2 FeCl 5 ·H 2 O single crystals and evaporated thin films for photodetectors1Machine learning force fields for inorganic crystalline materials: principles, advances, and emerging applications8Perovskite solar cells towards industrialization: overcoming challenges with data-driven strategies8Defect-Immune Perovskite Photovoltaics via Molecular Vise Engineering: A Pathway to Scalable, Encapsulation-Free Production under Mild Conditions0Organic–Inorganic Metal Halide Perovskites: Toward Stability, Chirality, and AI-Guided Discovery1Extracting Knowledge of 2D‐Hybrid Halide Perovskite Materials: A Data Mining Approach0Origin of suppressed ferroelectricity in κ-Ga2O3: interplay between polarization and lattice domain walls4Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces2Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications14Recent Progress of Metal Halide Perovskite‐Based S‐Scheme Heterojunction for Solar Energy Conversion0

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