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RESEARCH PAPER · Japanese Journal of Applied Physics · 2019

A high throughput molecular screening for organic electronics via machine learning: present status and perspective

Akinori Saeki, Kakaraparthi Kranthiraja

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A high throughput molecular screening for organic electronics via machine learning: present status and perspectiveOriginalAnalysis of trade-off relationships between resolution, line edge roughness, and sensitivity in extreme ultraviolet lithography using lasso regression10Machine learning method to predict threshold voltage distribution by read disturbance in 3D NAND Flash Memories10Machine-Learned Charge Transfer Integrals for Multiscale Simulations in Organic Thin Films42Evaluation-oriented exploration of photo energy conversion systems: from fundamental optoelectronics and material screening to the combination with data science84Exchange Spin Coupling from Gaussian Process Regression27Review of porphyrin-based photodynamic therapy materials2Review of Porphyrin Derivatives and Application in Photodynamic Therapy2High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68Morphological design strategies to tailor out-of-plane charge transport in conjugated polymer systems for device applications14Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells94Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning22Active discovery of organic semiconductors135OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors45Analysis of dissolution kinetics of narrow polydispersity poly(4-hydroxystyrene) in alkaline aqueous solution using machine learning15Computational Identification of Novel Families of Nonfullerene Acceptors by Modification of Known Compounds31Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries58High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Advanced Molecular Simulation Method for Boron Based Thermally Activated Delayed Fluorescence Materials by Using Autoqsar Calculation0Data-driven design of novel halide perovskite alloys73Impact of substituents on the performance of small-molecule semiconductors in organic photovoltaic devices via regulating morphology19Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Computational chemistry advances on benzodithiophene-based organic photovoltaic materials12Feasibility of p-Doped Molecular Crystals as Transparent Conductive Electrodes via Virtual Screening9The optical spectra of DMAC‐based molecules for organic light‐emitting diodes: Hybrid‐exchange density functional theory study4Machine Learning-Assisted Polymer Design for Improving the Performance of Non-Fullerene Organic Solar Cells34Machine Learning for the Design of Novel OLED Materials10Mapping the Progress in Natural Dye‐Sensitized Solar Cells: Materials, Parameters and Durability21Computational study of optoelectronic properties of oxadiazole-based compounds for organic light emitting diodes4Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data27Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance38Organic crystal structure prediction and its application to materials design69Advancing energy storage through solubility prediction: leveraging the potential of deep learning6Investigating magnetic van der Waals materials using data-driven approaches12Machine learning in energy chemistry: introduction, challenges and perspectives31Machine learning of atomic force microscopy images of organic solar cells20Classification of dissolution modes of partially protected poly(4-hydroxystyrene) in tetraalkylammonium hydroxide aqueous solutions13Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts6An Intelligent, User‐Inclusive Pipeline for Organic Semiconductor Design9High-Throughput Screening of Promising Redox-Active Molecules with MolGAT10Predictions of photophysical properties of phosphorescent platinum( II ) complexes based on ensemble machine learning approach20Efficient screening framework for organic solar cells with deep learning and ensemble learning48Exploration of Solution-Processed Bi/Sb Solar Cells by Automated Robotic Experiments Equipped with Microwave Conductivity18Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors9AI-assisted chemistry research: a comprehensive analysis of evolutionary paths and hotspots through knowledge graphs9Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices28Exploring and Proposing of Donor Molecules for Fullerene-Based Organic Thin-Film Solar Cells Using Machine Learning0Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry93Artificial Intelligence for Conjugated Polymers39Advances in materials informatics: a review42Machine Learning Algorithm for Artificial Intelligence-Based Precise Structural Modeling in Organic Light-Emitting Diodes30Design and Synthesis of Red-Absorbing Fluoran Leuco Dyes Supported by Computational Screening2Exploring the potential of QSAR in the discovery of novel green TADF materials: an experimental and theoretical study1A semi-automated quantum-mechanical workflow for the generation of molecular monolayers and aggregates4Machine learning approach for predicting high JSC donor molecules in fullerene-typed organic solar cells8Rapid Prototyping for Accelerated Establishment of Film Processing‐Performance Relationships in Silicon Phthalocyanine OFETs1Automated high-throughput organic crystal structure prediction via population-based sampling8Machine‐Learning‐Assisted Process Optimization for High‐Performance Organic Thermoelectrics13Thermally activated delayed fluorescence sensitizer for high Förster energy transfer efficiency in blue hyper-fluorescent organic light-emitting diodes7Generative design of functional organic molecules for terahertz radiation detection1Introduction to Predicting Properties of Organic Materials1Multi-level Δ-learning for predicting the radiative decay rate constant of phosphorescent platinum( ii ) complexes3A general protocol for phosphorescent platinum(II) complexes: generation, high throughput virtual screening and highly accurate predictions4Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning18Integrating AI into OLED material design: a comprehensive review of computational frameworks, challenges, and opportunities16Interpretable deep learning for XRD pattern analysis in lithium-ion batteries CNN and attention-based feature extraction6Evaporation-Induced Stratification in Nanoparticle Coatings: A Panoramic Overview of Single-Step Processes to Manufacture Multifunctional Films2A Machine Learning Approach to Determine the Band Gap Energy of High-Entropy Oxides Using UV-Vis Spectroscopy0AI-powered OLEDs: speeding up innovation in displays3Structure prediction of porous organic crystals2Predicting S 1 TDDFT Energies from ZINDO Calculations Using Message-Passing ΔML with Electronically Informed Descriptors5Machine Learning-Assisted Discovery of Chiral Azobenzene Dopants for Liquid Crystals0

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  2. Machine learning method to predict threshold voltage distribution by read disturbance in 3D NAND Flash Memories

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  3. Machine-Learned Charge Transfer Integrals for Multiscale Simulations in Organic Thin Films

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  4. Evaluation-oriented exploration of photo energy conversion systems: from fundamental optoelectronics and material screening to the combination with data science

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  5. Exchange Spin Coupling from Gaussian Process Regression

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  7. Review of Porphyrin Derivatives and Application in Photodynamic Therapy

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  8. High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

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  9. Morphological design strategies to tailor out-of-plane charge transport in conjugated polymer systems for device applications

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  10. Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells

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  11. Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning

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  14. Analysis of dissolution kinetics of narrow polydispersity poly(4-hydroxystyrene) in alkaline aqueous solution using machine learning

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  15. Computational Identification of Novel Families of Nonfullerene Acceptors by Modification of Known Compounds

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  16. Phase–Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries

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  17. High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications

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  18. Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks

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  19. Advanced Molecular Simulation Method for Boron Based Thermally Activated Delayed Fluorescence Materials by Using Autoqsar Calculation

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  20. Data-driven design of novel halide perovskite alloys

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  21. Impact of substituents on the performance of small-molecule semiconductors in organic photovoltaic devices via regulating morphology

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  22. Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics

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  23. Computational chemistry advances on benzodithiophene-based organic photovoltaic materials

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  25. The optical spectra of DMAC‐based molecules for organic light‐emitting diodes: Hybrid‐exchange density functional theory study

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  26. Machine Learning-Assisted Polymer Design for Improving the Performance of Non-Fullerene Organic Solar Cells

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