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RESEARCH PAPER · Journal of Materials Informatics · 2022

Recent progress in the data-driven discovery of novel photovoltaic materials

Lu Tian, Minjie Li, Wencong Lu, Tong‐Yi Zhang

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Recent progress in the data-driven discovery of novel photovoltaic materialsOriginalOpportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells25Feature Selection in Machine Learning for Perovskite Materials Design and Discovery59Impact of symmetry breaking on the performance of non-fullerene acceptors (NFAs) for photo and thermally stable organic solar cells (OSCs): A DFT-based interrogation and investigation36Machine Learning Combined with Weighted Voting Regression and Proactive Searching Progress to Discover ABO3-δ Perovskites with High Oxide Ionic Conductivity11OCPMDM 2.0: An intelligent solution for materials data mining6Inverse design of Bulk metallic glasses with reduced glass transform temperatures via Target-Optimal inverse projection3Data-driven optimization and machine learning analysis of compatible molecules for halide perovskite material22Copper-based perovskites and perovskite-like halides: A review from the perspective of molecular level86The mastery of details in the workflow of materials machine learning64Artificial intelligence enabled smart design and manufacturing of advanced materials: The endless Frontier in AI + era51Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles8NJmat: Data-Driven Machine Learning Interface to Accelerate Material Design12Study on the impact of hybridization of spherical fillers with size differences and sheet fillers on the thermal conductivity and anticorrosive performance of composite coatings17Two-dimensional XYN3 (X=V, Nb, Ta; Y=Si, Ge): Promising optoelectronic materials in photovoltaic photodetectors10Machine learning assisted crystallographic reconstruction from atom probe tomographic images2Fast parameter optimization for high-fidelity crystal plasticity simulation using active learning5Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures22Alkyl-chain engineering of bifunctional amino acids in optoelectronically superior lead-free copper-based perovskite Cs 3 Cu 2 I 51Deep eutectic solvent (DES)-polymer hybrid systems as tools in drug delivery0Design of simple-structured conjugated polymers for organic solar cells by machine learning-assisted structural modification and experimental validation2Wavelength-selective thermal nonreciprocity barely improves sky radiative cooling13Discovering novel lead-free solder alloy by multi-objective Bayesian active learning with experimental uncertainty21Unveiling next-generation organic photovoltaics: Quantum mechanical insights into non-fullerene donor–acceptor compounds9DFT and machine learning integration to predict efficiency of modified metal-free dyes in DSSCs5Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation48Progress in MXene-based photothermal materials for solar-driven water evaporation and desalination59The magneto-electronic, mechanical, structural, transport, and optoelectronic attributes of A2FeB′F6 (A = K, Na: B′ = Ag, Au) fluoroperovskites: Spin-polarized calculations11Accelerated discovery of high-performance small-molecule hole transport materials via molecular splicing, high-throughput screening, and machine learning11Chemical modification-induced enhancements in quantum dot photovoltaics: a theoretical and molecular descriptive analysis4Machine learning-enabled optoelectronic material discovery: a comprehensive review31Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review9A Computational Renaissance in High-Energy Density Materials (HEDMs) Research38Scientific Claims Under Distribution Shift: A Conceptual Theory for Robust Materials AI Inference0Predicting Materials Using Variational Autoencoders: A Systematic Literature Review8A crystal graph convolutional neural network framework for predicting stacking fault energy in concentrated alloys1Predicting power conversion efficiency in donor-acceptor pairs for organic solar cells using machine learning ensemble models5Knowledge-enabled data-driven smart design of ultra-strong ductile near-α titanium alloys under extreme conditions0Performance optimization of CIGS thin-film solar cells via charge transport layer engineering: SCAPS-1D simulations coupled with machine learning3Recent Advances in Artificial Intelligence in Organic Electronic Research1MatterMind: A User‐Friendly Material Design Platform Assisted by Large Language Model0Trends and future directions in computational studies of DSSCs: A systematic bibliometric study0Uncertainty-aware multi-source modeling for materials property prediction: A case study in ferroelectrics0Interpretable data-driven design of Ge-Sb-Se-Te thin films for energy-efficient and multilevel nonvolatile photonic memory0PVmatAgent: A Large Language Model (LLM) Agent for Perovskite Photovoltaic Material Design and Analysis0

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  2. Feature Selection in Machine Learning for Perovskite Materials Design and Discovery

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  3. Impact of symmetry breaking on the performance of non-fullerene acceptors (NFAs) for photo and thermally stable organic solar cells (OSCs): A DFT-based interrogation and investigation

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  18. Alkyl-chain engineering of bifunctional amino acids in optoelectronically superior lead-free copper-based perovskite Cs 3 Cu 2 I 5

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