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RESEARCH PAPER · Energy & Environmental Science · 2021

Accelerating organic solar cell material's discovery: high-throughput screening and big data

Xabier Rodríguez‐Martínez, Enrique Pascual‐San‐José, Mariano Campoy‐Quiles

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Accelerating organic solar cell material's discovery: high-throughput screening and big dataOriginalA time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection205Colour-Tuneable Hybrid Heterojunctions as Semi-Transparent Photovoltaic Windows for Photoelectrochemical Water-Splitting0Understanding the Microstructure Formation of Polymer Films by Spontaneous Solution Spreading Coating with a High‐Throughput Engineering Platform31Eliminating the Detrimental Effect of Secondary Doping on PEDOT : PSS Hole Transporting Material Performance12Dual Interface Protection for High Performance and Excellent Long-Term Stability of Organic Solar Cells10Color-tunable hybrid heterojunctions as semi-transparent photovoltaic windows for photoelectrochemical water splitting6High‐Throughput Screening of Blade‐Coated Polymer:Polymer Solar Cells: Solvent Determines Achievable Performance13Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98Defining inkjet printing conditions of superconducting cuprate films through machine learning14High-entropy alloy catalysts: high-throughput and machine learning-driven design59Identifying structure–absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics59Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors39Matching electron transport layers with a non-halogenated and low synthetic complexity polymer:fullerene blend for efficient outdoor and indoor organic photovoltaics15Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells25The Solution is the Solution: Data-Driven Elucidation of Solution-to-Device Feature Transfer for π-Conjugated Polymer Semiconductors28Synergistic enhancement in open-circuit voltage and photovoltaic performance via linear naphthyldithiophene building block3Interpretable machine learning for developing high-performance organic solar cells31Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Computational chemistry advances on benzodithiophene-based organic photovoltaic materials12Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies21High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective16Composition Gradient High-Throughput Polymer Libraries Enabled by Passive Mixing and Elevated Temperature Operability6Strategic structural evolution for enhancing the photovoltaic performance of quinoxaline-based polymers20Improved Predictions of Organic Photovoltaic Performance through Machine Learning Models Empowered by Artificially Generated Failure Data27Renewed Prospects for Organic Photovoltaics1052Obtention of solar cell parameters, through convergence of iterative cycles. Part 1: Theoretical analysis and cycles proposal19Printable high-efficiency organic ionic photovoltaic materials discovered by high-throughput first-principle calculations4Active Discovery of Donor:Acceptor Combinations For Efficient Organic Solar Cells10Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells26Cutting “lab-to-fab” short: high throughput optimization and process assessment in roll-to-roll slot die coating of printed photovoltaics27Machine learning of atomic force microscopy images of organic solar cells20Machine-learning-guided prediction of photovoltaic performance of non-fullerene organic solar cells using novel molecular and structural descriptors51Molecular Thin Films Enable the Synthesis and Screening of Nanoparticle Megalibraries Containing Millions of Catalysts25High‐Throughput Exploration of Triple‐Cation Perovskites via All‐in‐One Compositionally‐Graded Films12Orders of coupling representations as a versatile framework for machine learning from sparse data in high-dimensional spaces14Directed Message Passing Neural Network for Predicting Power Conversion Efficiency in Organic Solar Cells17Hexanary blends: a strategy towards thermally stable organic photovoltaics48State of the Art and Outlook of Data Science and Machine Learning in Organic Chemistry1The Synergistic Effects between Liquid Crystal and Crystalline Phase on Photo‐Responsive Elastomers toward Quick Photo‐Responsive Performance7Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline22Neural Network with Optimal Neuron Activation Functions Based on Additive Gaussian Process Regression27Exploration 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 Transistors9Identifying the Quantitative Relationship Between the Molecular Structure and the Horizontal Transition Dipole Orientation of TADF Emitters13Ground-State Orbital Analysis Predicts S 1 Charge Transfer in Donor–Acceptor Materials14Differential microthermometry enables high-throughput calorimetry2End-capped engineering of Quinoxaline core-based non-fullerene acceptor materials with improved power conversion efficiency20Accelerating the generation and discovery of high-performance donor materials for organic solar cells by deep learning15Combinatorial screening of wide band-gap organic solar cell materials with open-circuit voltage between 1.1 and 1.4 V19Nature-inspired and computer-aided approaches to enable improved photovoltaic materials, more efficient processing, and novel devices0Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry93Boosting the Performances of Semitransparent Organic Photovoltaics via Synergetic Near‐Infrared Light Management50Machine learning in the era of smart automation for renewable energy materials9Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics64Advances in theoretical calculations of organic thermoelectric materials8Machine Learning Study on the Virtual Screening of Donor–Acceptor Pairs for Organic Solar Cells13An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations84Roadmap on data-centric materials science27High‐Throughput Screening of Low‐Bandgap Organic Semiconductors for Photovoltaic Applications: In the Search of Correlations10Machine learning-enabled discovery of multi-resonance TADF molecules: Unraveling PLQY predictions from molecular structures21Molecular Design and High‐Throughput Virtual Screening of Electron Donor and Non‐fullerene Acceptors for Organic Solar Cells11Crystal Structure Prediction and Performance Assessment of Hydrogen Storage Materials: Insights from Computational Materials Science40Design and Synthesis of Red-Absorbing Fluoran Leuco Dyes Supported by Computational Screening2Harnessing Compositional Gradients to Elucidate Phase Behaviors toward High Performance Polymer Semiconductor Blends3Predicting PbS Colloidal Quantum Dot Solar Cell Parameters Using Neural Networks Trained on Experimental Data6Predictive Modeling and Design of Organic Solar Cells: A Data-Driven Approach for Material Innovation19Physics-Informed Machine Learning with Data-Driven Equations for Predicting Organic Solar Cell Performance21An overview of solar cell simulation tools30Rapid Prototyping for Accelerated Establishment of Film Processing‐Performance Relationships in Silicon Phthalocyanine OFETs1Machine‐Learning‐Assisted Process Optimization for High‐Performance Organic Thermoelectrics13Machine learning for a sustainable energy future15Utilizing large language models for identifying future research opportunities in environmental science14Synergic donor/acceptor pair fingerprint-embedding generation for machine learning enhancement in organic solar cells2BiBERTa: a self-supervised framework for accelerating the discovery of stable organic photovoltaic materials1Bridging lab-to-fab gaps: recent advances in the reproducibility of organic solar cells4Molecular design of organic photovoltaic donors and non-fullerene acceptors: a combined machine learning and genetic algorithm approach13On the impact of selective donor:acceptor structural ordering in PBDB-T:ITIC organic solar cells6Physics-guided machine learning of excited-state properties for the design of high-performance TADF emitters2Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm86A High Throughput Platform to Minimize Voltage and Fill Factor Losses8Roadmap on metal-halide perovskite semiconductors and devices17A Review of Machine Learning in Organic Solar Cells20Lyotropic Liquid Crystal Mediated Assembly of Donor Polymers Enhances Efficiency and Stability of Blade‐Coated Organic Solar Cells17The Value of Device Characterization for the Optimization of Organic Solar Cells2What Makes Thickness‐Tolerant Organic Solar Cells?15Strategies and Mechanisms To Minimize Energy Loss in Non-Fullerene Bulk Heterojunction Organic Solar Cells: Experimental and Computational Approaches11Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning18Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints28A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research5Stability of Organic Photovoltaics: From Root Causes to Advanced Analytical Techniques24AI-Driven Approaches for Next-Gen Solar Cells1Discovering novel halide perovskites using generative adversarial networks and random forest regression4Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning7Machine learning in polymer science: A new lens for physical and chemical exploration35Research progress on conjugated carbonyl polymer electrodes for organic lithium batteries7High-Throughput Molecular Design of Donors and Non-Fullerene Acceptors for Organic Solar Cells Based on Convolutional Neural Networks12Machine learning-driven prediction of organic solar cell performance: a data-centric approach to molecular design5Probing All‐Solid‐State Batteries with Real‐Time Synchrotron and Neutron Techniques14OSC-Net: a multi-fidelity machine learning model for organic solar cells5A high-throughput screening frame of polymer donor for efficient organic solar cell constructed by machine learning with encoded frequency molecular fingerprint2Multifunctional polymers in perovskite photovoltaics: bridging efficiency, stability, and manufacturing12Integration of materials science and artificial intelligence: From high‐throughput screening to autonomous laboratories15From fragments to function: data-driven design of high-performance non-fullerene acceptors for organic photovoltaics2Revolution by AI: Clean Energy Production and Utilization0High-throughput DoE synthesis of chalcones and pyrazolines for fluorescent sensing3Combinatorial to autonomous experimentation0Neural Networks with Rules-Based Parameters and Without Nonlinear Optimization: Comparison of Fixed-Shaped and Optimized Neuron Activation Functions0Accelerating the Development of Organic Solar Cells: A Standardized Protocol with Machine Learning Integration3Isostructural transition metal diselenide lead-free semiconductors with coupled optical, piezoelectric and energy conversion functionalities0Physics-Guided Inverse Design of Nonfullerene Acceptors via a Deep-Learning-Accelerated Genetic Algorithm0Molten Salt Engineering: Resolving the Contradiction and Core Challenges Between High Temperature and Precise Regulation to Achieve Green Synthesis of Advanced Nanomaterials2Pathways to commercially viable organic photovoltaic materials12Interpretable prediction model for organic solar cell performance: A principal component analysis-based investigation of feature importance1Evaluating MS-ANI vs SS-ANI for Surface Hopping Simulations: A Cyclohexadiene Photochemical Ring-Opening Case Study2Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces2Toward autonomous discovery in small-molecule organic photovoltaics: A review integrating artificial intelligence and machine learning within the molecular design landscape0Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding0Toward in-line monitoring of the microstructure of organic semiconductor materials for photovoltaic applications via spectroscopic ellipsometry0Unraveling the design rules for enhancing stability of organic photovoltaic materials under accelerated aging conditions through machine learning0Predicting the dynamic behavior of memristive devices using recurrent neural networks0Closed‐Loop Multi‐Objective Bayesian Optimization of High‐Dimensional Processing Spaces for Organic Solar Cells0Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production1Strategy to Screen Donor and Acceptor Pairs for Organic Solar Cells Through Machine Learning0From Molecular Design to Device Performance: DFT and TD ‐ DFT Insights into Small‐Molecule Organic Solar Cells0High‐Throughput Optimization of Organic Photovoltaics for Performance and Stability0AI-driven modeling of modulated magnetoelectric magnetic field sensors0Identifying a Figure of Merit for Solvent Optimization in Non‐Fullerene Organic Solar Cells via High‐Throughput Experiments0

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  48. End-capped engineering of Quinoxaline core-based non-fullerene acceptor materials with improved power conversion efficiency

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  50. Combinatorial screening of wide band-gap organic solar cell materials with open-circuit voltage between 1.1 and 1.4 V

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    Miquel Casademont‐Viñas, Daniel Capolat, Arianna Quesada-Ramírez, Matiss Reinfelds, Gregor Trimmel, Matteo Sanviti, Jaime Martín, Alejandro Rodolfo Goñi, Thomas Kirchartz, Mariano Campoy‐Quiles

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