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RESEARCH PAPER · npj Computational Materials · 2020

Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cells

Yao Wu, Jie Guo, Rui Sun, Jie Min

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Machine learning for accelerating the discovery of high-performance donor/acceptor pairs in non-fullerene organic solar cellsOriginalMachine learning for high performance organic solar cells: current scenario and future prospects375Accelerated discovery of stable spinels in energy systems via machine learning56A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection205Accelerating organic solar cell material's discovery: high-throughput screening and big data129High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68Machine learning for advanced energy materials208Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells94Applying machine learning to balance performance and stability of high energy density materials67Artificial intelligence to support the integration of variable renewable energy sources to the power system214Toward Real Setting Applications of Organic and Perovskite Solar Cells: A Comparative Review65Big data and machine learning for materials science180Simultaneous Optimization of Donor/Acceptor Pairs and Device Specifications for Nonfullerene Organic Solar Cells Using a QSPR Model with Morphological Descriptors26Benzothiadiazole-based non-fullerene acceptors206Computational Identification of Novel Families of Nonfullerene Acceptors by Modification of Known Compounds31Algorithmically extracted morphology descriptions for predicting device performance6Multi‐Fidelity High‐Throughput Optimization of Electrical Conductivity in P3HT‐CNT Composites58Copolymer Informatics with Multitask Deep Neural Networks101Data-assisted polymer retrosynthesis planning31Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors27Unsupervised discovery of thin-film photovoltaic materials from unlabeled data37Capture the high-efficiency non-fullerene ternary organic solar cells formula by machine-learning-assisted energy-level alignment optimization25Molecular Conditional Generation and Property Analysis of Non-Fullerene Acceptors with Deep Learning10Organic Solar Cells Parameters Extraction and Characterization Techniques9Machine learning and microstructure design of polymer nanocomposites for energy storage application46Prediction of non-radiative voltage losses in organic solar cells using machine learning33Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells90Novel Materials for Urban Farming91Data-driven thermoelectric modeling: Current challenges and prospects25Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Accelerated exploration of efficient ternary solar cells with PTB7:PC 71 BM:SMPV1 using machine-learning methods14High-Performance Solar Cells by Machine Learning and Pareto Optimality0Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors39Machine 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% efficiency193Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells25Recent progress in the data-driven discovery of novel photovoltaic materials44High‐Efficiency Non‐Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry78Fabrication and characterization of organic solar cells using non-fullerene acceptors0Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches55Design for a Sustainability Approach to Organic Solar Cell Design: the Use of Machine Learning to Quantify the Trade-off between Performance, Stability, and Environmental Impact8Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory23Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Beyond Woodward–Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy20Organic Photovoltaic Efficiency Predictor: Data-Driven Models for Non-Fullerene Acceptor Organic Solar Cells37Machine Learning on a Robotic Platform for the Design of Polymer–Protein Hybrids177Machine Learning-Assisted Polymer Design for Improving the Performance of Non-Fullerene Organic Solar Cells34Machine Learning for Organic Photovoltaic Polymers: A Minireview162Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs56Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data27A Theoretical Perspective on the Thermodynamic Stability of Polymer Blends for Solar Cells: From Experiments to Predictive Modeling16Active Discovery of Donor:Acceptor Combinations For Efficient Organic Solar Cells10Novel structural feature-descriptor platform for machine learning to accelerate the development of organic photovoltaics31Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells26CatBoost-Based Framework for Intelligent Prediction and Reaction Condition Analysis of Coupling Reaction2High-Performance Solar Cells by Machine Learning and Pareto Optimality0Machine Learning for Next‐Generation Functional Materials3The Rise of Machine Learning in Polymer Discovery85Virtual screening of efficient building blocks and designing of new polymers for organic solar cells12Screening Efficient Tandem Organic Solar Cells with Machine Learning and Genetic Algorithms44Prediction of large magnetic moment materials with graph neural networks and random forests18Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts6Interpretable 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 Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells40Data-driven approaches enabling the screening and design of promising materials for energy storage batteries0Directed Message Passing Neural Network for Predicting Power Conversion Efficiency in Organic Solar Cells17Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline22Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models15Machine learning-assisted performance prediction and molecular design of all-small-molecule organic solar cells based on the Y6 acceptor23Machine learning study on organic solar cells and virtual screening of designed non-fullerene acceptors19Efficient screening framework for organic solar cells with deep learning and ensemble learning48Electrochemical Sensor to Detect Antibiotics in Milk Based on Machine Learning Algorithms61Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques17Predicting and analyzing the fill factor of non-fullerene organic solar cells based on material properties and interpretable machine-learning strategies36Multicrystalline Informatics Applied to Multicrystalline Silicon for Unraveling the Microscopic Root Cause of Dislocation Generation18Ground-State Orbital Analysis Predicts S 1 Charge Transfer in Donor–Acceptor Materials14Investigation of the open-circuit voltage of non-fullerene acceptors-based ternary organic solar cells based on interpretable machine-learning approach and chemically inspired descriptors11Accelerating the generation and discovery of high-performance donor materials for organic solar cells by deep learning15Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices28Design and virtual screening of donor and non-fullerene acceptor for organic solar cells using long short-term memory model21Machine learning in the era of smart automation for renewable energy materials9Photovoltaic conversion of organic photovoltaic materials based on molecular fingerprint feature engineering0A bibliometric analysis of machine learning techniques in photovoltaic cells and solar energy (2014–2022)42Data Cleansing and Sub‐Unit‐Based Molecular Description Enable Accurate Prediction of The Energy Levels of Non‐Fullerene Acceptors Used in Organic Solar Cells9Machine Learning Study on the Virtual Screening of Donor–Acceptor Pairs for Organic Solar Cells13Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells57High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data13Multicomponent organic blend systems: A review of quaternary organic photovoltaics15A systematic approach to generate polymer library: A search for polymers with high dipole moment1Explainable Prediction of Hydrophilic/Hydrophobic Property of Polymer Brush Surfaces by Chemical Modeling and Machine Learning21The mastery of details in the workflow of materials machine learning64Predicting Emission Wavelengths in Benzobisoxazole-Based OLEDs with Gradient Boosted Ensemble Models3Interpretable AI and Machine Learning Classification for Identifying High-Efficiency Donor–Acceptor Pairs in Organic Solar Cells11Accelerating the discovery of acceptor materials for organic solar cells by deep learning29A review on machine learning-guided design of energy materials19Progress of organic photovoltaics towards 20% efficiency132Modeling of stable and novel organic materials for high efficiency opv cells0Construction frontier molecular orbital prediction model with transfer learning for organic materials36Design of experiments with the support of machine learning for process parameter optimization of all‐small‐molecule organic solar cells53Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development102Predictive Modeling and Design of Organic Solar Cells: A Data-Driven Approach for Material Innovation19Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review12Combination of Transfer Learning and Chemprop Interpreter with Support of Deep Learning for the Energy Levels of Organic Photovoltaic Materials Prediction and Regulation11A review of photostability in organic solar cells: from mechanisms to improvement strategies12BiBERTa: a self-supervised framework for accelerating the discovery of stable organic photovoltaic materials1High throughput molecular design of electron donors and non-fullerene acceptors using machine learning combined with substructure importance8Molecular design of organic photovoltaic donors and non-fullerene acceptors: a combined machine learning and genetic algorithm approach13One more step towards better stability of non-fullerene organic solar cells: advances, challenges, future perspectives, and the Era of artificial intelligence97State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors16Designing Donors and Nonfullerene Acceptors for Organic Solar Cells Assisted by Machine Learning and Fragment‐Based Molecular Fingerprints14Development of Organic Semiconductor Materials for Organic Solar Cells via the Integration of Computational Quantum Chemistry and AI-Powered Machine Learning17MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization2A Review of Machine Learning in Organic Solar Cells20Revealing the Impact of CZTSe/CdS Interface Fluctuations on PV Device Performance through Big Data Analysis Assisted by Machine Learning Methods2Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation48In Situ Raman Spectra and Machine Learning Assistant Thermal Annealing Optimization for Effective Phototransistors5Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning18Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints28Active Learning‐Driven Discovery of Donor‐Acceptor Covalent Triazine Frameworks for High‐Performance Photocatalysts9The two-way street: How AI and clean energy affect each other9Advances in molecular materials and devices for intelligent perception applications2Computational Modeling of Indoor Organic Photovoltaics: Dataset Curation, Predictive Analysis, and Machine Learning Approaches4To be Artificial Intelligence for sustainability or not to be sustainable Artificial Intelligence14High-Throughput Molecular Design of Donors and Non-Fullerene Acceptors for Organic Solar Cells Based on Convolutional Neural Networks12Cu2O hole transport layer optimization for Sb2(S,Se)3 solar cells: combining machine learning prediction with experimental validation1Machine learning-assisted prediction of organic solar cell efficiency from device reflectance spectra1Artificial intelligence in renewable energy technologies and sustainable transition0OPECM: A comprehensive dataset for organic polymer energy conversion materials enabling data-driven interpretative analysis0Role of AI in Solar Cell Materials Research: A Review0Ultralow Critical Ratio of Near-Infrared Donors in Ternary Organic Phototransistors for Ultrahigh-Performance Near-Infrared Photodetection2Solid additive engineering for high-efficiency organic solar cells: categories, mechanisms, and perspectives2A Set of Complementary Descriptors for the Power Conversion Efficiency Predictions of Organic Solar Cells2Artificial intelligence in the discovery and design of molecular semiconductors: a systematic review3Decoding non-fullerene acceptors for organic solar cells: a theoretical structure–property–performance perspective1Fragment-LEGO strategy toward seven-ring-fused acceptors for organic solar cells0Organic Photovoltaics Spectral Overlap Prediction Based on Electronic Structure Descriptors and Explainable Machine Learning0Structure-guided machine learning for efficiency prediction of organic photovoltaics using experimentally informed molecular descriptors3Towards an artificial intelligence ecosystem for performance-driven polymer design1Predicting power conversion efficiency of dyes in dye-sensitised solar cells0Bowl‐Shaped Conjugated Organic Semiconductors: From Structural Design, Molecular Assembly to Device Applications10Predicting power conversion efficiency in donor-acceptor pairs for organic solar cells using machine learning ensemble models5Descriptor Engineering for Machine-Learning-Based Performance Prediction in Organic Solar Cells: A Mini Review2Development of Machine Learning in Organic Photovoltaic Cells †8Navigating high-dimensional processing parameters in organic photovoltaics via a multitier machine learning framework3Multi-objective AI-driven optimization guides the discovery of high-performance organic photovoltaics0Achieving higher efficiency in solar panels using gold nanosphere arrays in structure of Grated CdS and advanced interfacial layers0Expert systems for predicting the efficiencies of photomultiplication organic photodetectors0Toward autonomous discovery in small-molecule organic photovoltaics: A review integrating artificial intelligence and machine learning within the molecular design landscape0Concurrent Optimization of Device Architecture, Transport Layers, and Active Layer for Organic Photovoltaics by Machine Learning0Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding0Reconstructing Solar Materials Innovation Through Data Science: A Review of Opportunities and Structural Bottlenecks2Modeling Multilevel Donor–Acceptor Interactions for Device‐Level Efficiency Prediction in Organic Solar Cells0

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