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RESEARCH PAPER · Science Advances · 2019

Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

Wenbo Sun, Yujie Zheng, Ke Yang, Qi Zhang, Akeel Abbas Shah, Zhou Wu, Yuyang Sun, Liang Feng, Dongyang Chen, Zeyun Xiao, Shirong Lu, Yong Li, Kuan Sun

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Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materialsOriginalCost-effective materials discovery: Bayesian optimization across multiple information sources38Recent advances of computational chemistry in organic solar cell research99Toward ideal hole transport materials: a review on recent progress in dopant-free hole transport materials for fabricating efficient and stable perovskite solar cells388Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191Assessment of Reproducibility, Hysteresis, and Stability Relations in Perovskite Solar Cells Using Machine Learning51Organic Photovoltaics: Relating Chemical Structure, Local Morphology, and Electronic Properties60Molecular Lock Induced by Chloroplatinic Acid Doping of PEDOT:PSS for High-Performance Organic Photovoltaics39Synergy of Liquid‐Crystalline Small‐Molecule and Polymeric Donors Delivers Uncommon Morphology Evolution and 16.6% Efficiency Organic Photovoltaics87Machine Learning Based Distinguishing between Ferroelectric and Non‐Ferroelectric Polarization–Electric Field Hysteresis Loops12Machine-Learned Charge Transfer Integrals for Multiscale Simulations in Organic Thin Films42Development Status and Prospects of Artificial Intelligence in the Field of Energy Conversion Materials14Hole (donor) and electron (acceptor) transporting organic semiconductors for bulk-heterojunction solar cells91Singlet oxygen generation by porphyrins and metalloporphyrins revisited: A quantitative structure-property relationship (QSPR) study63Causal Inference Machine Learning Leads Original Experimental Discovery in CdSe/CdS Core/Shell Nanoparticles24Predicting Inorganic Photovoltaic Materials with Efficiencies >26% via Structure-Relevant Machine Learning and Density Functional Calculations28Electronic Properties and Photocatalytic Hydrogen Evolution Rates for Alternating Conjugated Copolymers: Predictions and Insights by Data-Driven Models0Segment Descriptor Enabling Prediction of Electronic Properties and Photocatalytic Hydrogen Evolution Rate of Alternating Conjugated Copolymers Based on Machine Learning1Machine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields274Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches42Research progress in molecular dynamics simulation of CNT and graphene reinforced metal matrix composites17Small Data Machine Learning: Classification and Prediction of Poly(ethylene terephthalate) Stabilizers Using Molecular Descriptors32Machine learning for high performance organic solar cells: current scenario and future prospects375The Path to 20% Power Conversion Efficiencies in Nonfullerene Acceptor Organic Solar Cells221Accelerating materials discovery using machine learning238A chlorinated copolymer donor demonstrates a 18.13% power conversion efficiency212A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection205Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning57Data-driven algorithms for inverse design of polymers100Designs and understanding of small molecule-based non-fullerene acceptors for realizing commercially viable organic photovoltaics52High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68Intelligent predicting reaction performance in multi-dimensional chemical space using quantile regression forest4Predicting the photocurrent–composition dependence in organic solar cells50Recent advances in PM6:Y6-based organic solar cells231Temporal-spatial-energy resolved advance multidimensional techniques to probe photovoltaic materials from atomistic viewpoint for next-generation energy solutions20Transfer Learned Designer Polymers For Organic Solar Cells33Voting Data-Driven Regression Learning for Accelerating Discovery of Advanced Functional Materials and Applications to Two-Dimensional Ferroelectric Materials18Machine learning for advanced energy materials208Machine learning for perovskite materials design and discovery536Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields175Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning22Teaching Solid Mechanics to Artificial Intelligence: a fast solver for heterogeneous solids1Toward Real Setting Applications of Organic and Perovskite Solar Cells: A Comparative Review65The Prediction of the Undercooling Degree of As-Cast Irons and Aluminum Alloys via Machine Learning7Simultaneous Optimization of Donor/Acceptor Pairs and Device Specifications for Nonfullerene Organic Solar Cells Using a QSPR Model with Morphological Descriptors26Algorithmically extracted morphology descriptions for predicting device performance6Predicting Polymers’ Glass Transition Temperature by a Chemical Language Processing Model91Molecular fingerprint and machine learning to accelerate design of high‐performance homochiral metal–organic frameworks30Scalable Estimator of the Diversity for De Novo Molecular Generation Resulting in a More Robust QM Dataset (OD9) and a More Efficient Molecular Optimization.0Design, synthesis and control of the optical properties of phosphors for white light emitting diodes0Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials144CNN model optimization and intelligent balance model for material demand forecast23Noble-Metal-Free Multicomponent Nanointegration for Sustainable Energy Conversion264Machine Learning Force Fields: Recent Advances and Remaining Challenges127Machine Learning Guided Design of Single–Phase Hybrid Lead Halide White Phosphors25Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors27Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li–S Battery47Predicting Power Conversion Efficiency of Organic Photovoltaics: Models and Data Analysis39Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials46Applied Machine Learning for Developing Next‐Generation Functional Materials72Machine learning and microstructure design of polymer nanocomposites for energy storage application46Prediction of non-radiative voltage losses in organic solar cells using machine learning33Advanced machine learning decision policies for diameter control of carbon nanotubes35Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization12Machine Learning Driven Synthesis of Few-Layered WTe2 with Geometrical Control68The role of machine learning to boost the bioenergy and biofuels conversion170Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Machine Learning-Assisted Development of Sensitive Electrode Materials for Mixed Potential-Type NO2Gas Sensors34Novel Materials for Urban Farming91Role of artificial intelligence in chemistry26Organic Photovoltaic Efficiency Predictor: Data-Driven Predictions of Power Conversion Efficiencies of Non-Fullerene Acceptor Organic Solar Cells1XGBoost‐based intelligence yield prediction and reaction factors analysis of amination reaction26Molecular-fingerprint machine-learning-assisted design and prediction for high-performance MOFs for capture of NMHCs from air51Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning0Optimizing molecules using efficient queries from property evaluations74Beyond structural insight: a deep neural network for the prediction of Pt L 2/3 -edge X-ray absorption spectra12Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models1Impact of substituents on the performance of small-molecule semiconductors in organic photovoltaic devices via regulating morphology19Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th-based organic solar cells with over 15% efficiency193Machine Learning Strategies for the Structure-Property Relationship of Copolymers51Machine-learning-assisted prediction of long-term performance degradation on solid oxide fuel cell cathodes induced by chromium poisoning41Open-Circuit Voltage Loss and Dielectric Constants as New Descriptors in Machine Learning Study on Organic Photovoltaics11Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells25Prediction without Transferability: Domain Shift in Cross-Material AI Inference0Progress, challenges and perspectives of computational studies on glassy superionic conductors for solid-state batteries31Recent progress in the data-driven discovery of novel photovoltaic materials44Understanding the charge dynamics in organic light-emitting diodes using convolutional neural network16High‐Efficiency Non‐Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry78Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning39Volatile Solid Additive‐Assisted Sequential Deposition Enables 18.42% Efficiency in Organic Solar Cells136Review—Conjugated Polymer Photovoltaic Materials: Performance and Applications of Organic Semiconductors in Photovoltaics15Photoelectrochemical Properties, Machine Learning, and Symbolic Regression for Molecularly Engineered Halide Perovskite Materials in Water42Global instability index as a crystallographic stability descriptor of halide and chalcogenide perovskites43Interpretable machine learning for developing high-performance organic solar cells31Deep Transfer Learning: A Fast and Accurate Tool to Predict the Energy Levels of Donor Molecules for Organic Photovoltaics33How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells86A review on theoretical models for lithium–sulfur battery cathodes354Design for a Sustainability Approach to Organic Solar Cell Design: the Use of Machine Learning to Quantify the Trade-off between Performance, Stability, and Environmental Impact8Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review65Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network43Computational chemistry advances on benzodithiophene-based organic photovoltaic materials12Interpretable Machine Learning for Investigating Photoelectrochemical Properties of Cosensitizer-Based CH3NH3PbI3/TiO2 Films in Water11Lossless multi-scale constitutive elastic relations with artificial intelligence52Organic Photovoltaic Efficiency Predictor: Data-Driven Models for Non-Fullerene Acceptor Organic Solar Cells37Experimental search for high-performance ferroelectric tunnel junctions guided by machine learning10A combined clustering/symbolic regression framework for fluid property prediction36Recent Trends and Future Prospects of Materials and Process Informatics2Material machine learning for alloys: Applications, challenges and perspectives150A transfer learning approach for reaction discovery in small data situations using generative model15An emerging machine learning strategy for electrochemical sensor and supercapacitor using carbonized metal–organic framework58Deep Learning for Additive Screening in Perovskite Light‐Emitting Diodes5Deep Learning for Additive Screening in Perovskite Light‐Emitting Diodes40Machine learning enables interpretable discovery of innovative polymers for gas separation membranes237A Generative Approach to Materials Discovery, Design, and Optimization121Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models2VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling0Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models0Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects24Rapid calculation of internal conversion and intersystem crossing rate for organic materials discovery26Bond Energy Assists Accurate Molecule Property Prediction1The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches30Deep Learning-Based Layer Identification of 2D Nanomaterials8Machine learning assisted prediction of charge transfer properties in organic solar cells by using morphology-related descriptors20Machine learning for a sustainable energy future517Benzothiadiazole-based push-pull copolymers – Balancing synthetic complexity against organic solar cell efficiency12Deciphering the Role of Side‐Chain Engineering and Solvent Vapor Annealing for Binary All‐Small‐Molecule Organic Solar Cells42Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives113CIMG-BERT: Pretraining Bidirectional Transformers with Chemistry Knowledge for Molecular Property Prediction1Active Discovery of Donor:Acceptor Combinations For Efficient Organic Solar Cells10Novel structural feature-descriptor platform for machine learning to accelerate the development of organic photovoltaics31A computational method for the load spectra of large-scale structures with a data-driven learning algorithm12Crystal structural prediction of perovskite materials using machine learning: A comparative study20Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells26A polymer genome approach for rational design of poly(aryl ether)s with high glass transition temperature14An Introduction to Machine Learning in Molecular Sciences0CatBoost-Based Framework for Intelligent Prediction and Reaction Condition Analysis of Coupling Reaction2Design of polyimides with targeted glass transition temperature using a graph neural network50Machine Learning Application In Modeling Organic Pollutant Adsorption on Carbonaceous Materials: A Comprehensive Review with Statistical Insights1Machine learning assisted identification of the matched energy level of materials for high open circuit voltage in binary organic solar cells25Machine learning in energy chemistry: introduction, challenges and perspectives31Machine learning integrated photocatalysis: progress and challenges94Solar Cells and Relevant Machine Learning3Uncovering novel liquid organic hydrogen carriers: a systematic exploration of chemical compound space using cheminformatics and quantum chemical methods19Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics73Machine learning modeling for the prediction of plastic properties in metallic glasses43Machine‐Learning‐Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes161Unveiling the complex structure-property correlation of defects in 2D materials based on high throughput datasets40Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Machine-Learning-Assisted Screening of Interface Passivation Materials for Perovskite Solar Cells107

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