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RESEARCH PAPER · ACS Energy Letters · 2019

Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment Amines

Yongze Yu, Xuanheng Tan, Shougui Ning, Yiying Wu

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Machine Learning for Understanding Compatibility of Organic–Inorganic Hybrid Perovskites with Post-Treatment AminesOriginalMethylamine-induced defect-healing and cationic substitution: a new method for low-defect perovskite thin films and solar cells55Insights from Machine Learning Techniques for Predicting the Efficiency of Fullerene Derivatives‐Based Ternary Organic Solar Cells at Ternary Blend Design101Machine Learning for Understanding the Relationship between the Charge Transport Mobility and Electronic Energy Levels for n‐Type Organic Field‐Effect Transistors35Challenges and Directions for Green Chemical Engineering—Role of Nanoscale Materials25A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells41Performance and Matching Band Structure Analysis of Tandem Organic Solar Cells Using Machine Learning Approaches28Identification of host–guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study38Toward ideal hole transport materials: a review on recent progress in dopant-free hole transport materials for fabricating efficient and stable perovskite solar cells388Structural Deformation Controls Charge Losses in MAPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics87Efficient and stable perovskite solar cells via surface passivation of an ultrathin hydrophobic organic molecular layer65Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells102Ambient Pressure X-ray Photoelectron Spectroscopy Investigation of Thermally Stable Halide Perovskite Solar Cells via Post-Treatment65Machine learning for halide perovskite materials130Element Code from Pseudopotential as Efficient Descriptors for a Machine Learning Model to Explore Potential Lead-Free Halide Perovskites15Quantitative predictions of photo-emission dynamics in metal halide perovskites via machine learning3Machine learning in materials design: Algorithm and application*62Critical review of machine learning applications in perovskite solar research139Organic–inorganic hybrid and inorganic halide perovskites: structural and chemical engineering, interfaces and optoelectronic properties49Moisture‐Resistant FAPbI3 Perovskite Solar Cell with 22.25 % Power Conversion Efficiency through Pentafluorobenzyl Phosphonic Acid Passivation150Machine Learning Accelerated Insights of Perovskite Materials3Machine Learning and First-Principles Insights on Molecularly Modified CH 3NH 3PbI 3 Film in Water0Passivation and process engineering approaches of halide perovskite films for high efficiency and stability perovskite solar cells327Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells94Machine learning and symbolic regression investigation on stability of MXene materials83Is machine learning redefining the perovskite solar cells?59Predictive Design Model for Low-Dimensional Organic–Inorganic Halide Perovskites Assisted by Machine Learning114Machine Learning Roadmap for Perovskite Photovoltaics85Dependence between Structural and Electronic Properties of CsPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics48Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM‐43 Synthesis by Using Machine Learning6Accelerated discovery of defect tolerant organo-halide perovskites1Interactions between gas molecules and two-dimensional Ruddlesden–Popper halide perovskite3Bond length alternation- and data mining-assisted exploration of molecular adsorbates with π-conjugation and amines for two-dimensional halide perovskite surface2From Structural Design to Functional Construction: Amine Molecules in High‐Performance Formamidinium‐Based Perovskite Solar Cells143From Structural Design to Functional Construction: Amine Molecules in High‐Performance Formamidinium‐Based Perovskite Solar Cells22Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water42Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches55Quantitative Predictions of Moisture-Driven Photoemission Dynamics in Metal Halide Perovskites via Machine Learning30Interpretable Machine Learning for Investigating Photoelectrochemical Properties of Cosensitizer-Based CH3NH3PbI3/TiO2 Films in Water11Machine learning and first-principles insights on molecularly modified CH3NH3PbI3 film in water7Dye-modified halide perovskite materials6Machine learning and symbolic regression for adsorption of atmospheric molecules on low-dimensional TiO223Molecular design for perovskite solar cells5Deep Learning for Materials Recognition from FTIR Images0First-principles investigation on adsorption of anchors on two-dimensional halide perovskite material7Image-based machine learning for materials science54Topological feature engineering for machine learning based halide perovskite materials design71Accelerated innovation in developing high-performance metal halide perovskite solar cell using machine learning19Complete Solution-Processed Semitransparent and Flexible Organic Solar Cells: A Success of Polyimide/Ag-Nanowires- and PH1000-Based Electrodes with Plasmonic Enhanced Light Absorption8Localization control of 2D/3D perovskite heterostructures at grain boundaries by amine-vapor-induced dimensionality reduction6Geometric and topological AI for molecular sciences0Machine learning in energy chemistry: introduction, challenges and perspectives31Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Machine-Learning-Assisted Screening of Interface Passivation Materials for Perovskite Solar Cells107Machine learning investigation to predict the relationship between photoluminescence and crystalline properties of blue phosphor Ba0.9-xSrxMgAl10O17:Eu2+6High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap35Machine learning assisted classification of post-treatment amines for increasing the stability of organic-inorganic hybrid perovskites2Machine learning for perovskite solar cell design58First-principles and machine learning modeling on adsorption of atmospheric gases on two-dimensional Ruddlesden–Popper halide perovskite surface7Optimizing Photoelectrochemical Photovoltage and Stability of Molecular Interlayer-Modified Halide Perovskite in Water: Insights from Interpretable Machine Learning and Symbolic Regression14The role of machine learning in perovskite solar cell research42Design Rules for Two‐Dimensional Organic Semiconductor‐Incorporated Perovskites (OSiP) Gleaned from Thousands of Simulated Structures**11Design Rules for Two‐Dimensional Organic Semiconductor‐Incorporated Perovskites (OSiP) Gleaned from Thousands of Simulated Structures**0Interpretable machine-learning for predicting power conversion efficiency of non-halogenated green solvent-processed organic solar cells based on Hansen solubility parameters and molecular weights of polymers19Machine Learning-Assisted Defect Analysis and Optimization for P-I-N-Structured Perovskite Solar Cells21Machine learning for fast development of advanced energy materials23Machine Learning Prediction of High-Yield Cobalt- and Nickel-Catalyzed Borylations10A Data-Driven Platform for Two-Dimensional Hybrid Lead-Halide Perovskites18Emerging opportunities for hybrid perovskite solar cells using machine learning8Frontier Molecular Orbital Offset as an Empirical Descriptor for Predicting Short Circuit Current of Nonfullerene Organic Solar Cells12Machine Learning‐Accelerated Development of Perovskite Optoelectronics Toward Efficient Energy Harvesting and Conversion3Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives76Emerging Two-Dimensional Organic Semiconductor-Incorporated Perovskites─A Fascinating Family of Hybrid Electronic Materials101Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12Accelerated Multisolvent Prediction for Aqueous Stable Halide Perovskite Materials7Prediction 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 Cells5A present scenario of the computational approaches for ternary organic solar cells3Accelerated design of stable halide perovskite heterostructure film in hostile condition via surface modifier5Machine learning–assisted colloidal synthesis: A review39Explainable optimized 3D-MoRSE descriptors for the power conversion efficiency prediction of molecular passivated perovskite solar cells through machine learning5Perovskite single crystal SCLC measurement prediction using a machine learning model10X射线成像用零维无铅杂化卤化物闪烁体研究进展（特邀）4A machine learning framework for predicting device performance in 2D metal halide perovskite photodetector24Machine Learning Application for Nutrient Removal Rate Coefficient Analyses in Horizontal Flow Constructed Wetlands26Machine-Learning-Aided Understanding of Protein Adsorption on Zwitterionic Polymer Brushes44Versatile photo-sensing ability of paper based flexible 2D-Sb0.3Sn0.7Se2 photodetector and performance prediction with machine learning algorithm25Machine Learning Tools to Assist the Synthesis of Antibacterial Carbon Dots26Machine learning-based screening of two-dimensional perovskite organic spacers13Geometric data analysis-based machine learning for two-dimensional perovskite design12Bayesian reverse design of high-efficiency perovskite solar cells based on experimental knowledge constraints10A review on machine learning-guided design of energy materials19A Futuristic Approach to Subsurface-Constructed Wetland Design for the South-East Asian Region Using Machine Learning18Machine learning empowers efficient design of ternary organic solar cells with PM6 donor16Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices37Elevating perovskite efficiency via Machine Learning-Assisted screening of Passivators15Efficiently screening organic ligands by machine learning for stabilizing 2D/3D perovskites5Fabrication Strategies of Mn 2+ ‐Based Scintillation Screens for X‐Ray Detection and Imaging18Fabrication Strategies of Mn 2+ ‐Based Scintillation Screens for X‐Ray Detection and Imaging74Unraveling the thermodynamic origins and kinetic pathways of intermediate phase engineering in perovskite solar cells11Data‐Driven Molecular Encoding for Efficient Screening of Organic Additives in Perovskite Solar Cells12A hybrid machine learning approach for predicting the performance of perovskite solar cells under varying temperatures2Thermodynamic stability prediction of organic–inorganic hybrid perovskites using machine learning2Bridging artificial intelligence with photovoltaics7Algorithm-Guided Experimentation for Optimization of High-Performance Perovskite Solar Cells3GFDM descriptor-driven machine learning prediction of surface properties of molecular modified surfaces0Machine Learning Unlocks New Directions in Halide Perovskite Research6Resonance‐Hybridized Molecule Driven Perovskite Surface Sulfidation and Electron‐Transport Bridging for Efficient and Stable Inverted Perovskite Solar Cells9Machine learning-derived stage-specific design rules for metal-organic framework selection in seasonal hydrogen storage2Organic–Inorganic Metal Halide Perovskites: Toward Stability, Chirality, and AI-Guided Discovery1Novel perovskite semiconductors for radiation detection and dosimetry applications0Machine Learning Accelerated Design of Self-Assembled Monolayers for High-Performance Perovskite Solar Cells5Manganese(II)‐Based Luminescent Materials for Advanced Scintillation6

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