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RESEARCH PAPER · Joule · 2021

Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning

Xiaoyan Du, Larry Lüer, Thomas Heumueller, Jerrit Wagner, Christian G. Berger, Tobias Osterrieder, Jonas Wortmann, Stefan Langner, Uyxing Vongsaysy, Mélanie Bertrand, Ning Li, Tobias Stubhan, Jens Hauch, Christoph Josef Brabec

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Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine LearningOriginalA 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 data129Understanding the Microstructure Formation of Polymer Films by Spontaneous Solution Spreading Coating with a High‐Throughput Engineering Platform31The evolution of Materials Acceleration Platforms: toward the laboratory of the future with AMANDA70Review: materials and modelling for organic photovoltaic devices14Robots are taking up the challenges in photovoltaics R&D1High‐Throughput Robotic Synthesis and Photoluminescence Characterization of Aqueous Multinary Copper–Silver Indium Chalcogenide Quantum Dots22Applied Machine Learning for Developing Next‐Generation Functional Materials72Advances and prospective in thermally stable nonfullerene polymer solar cells47Flexible automation accelerates materials discovery98A bilayer conducting polymer structure for planar perovskite solar cells with over 1,400 hours operational stability at elevated temperatures229Opportunities 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 materials44Ternary organic solar cells: A review of the role of the third element193Toward autonomous materials research: Recent progress and future challenges61The Solution is the Solution: Data-Driven Elucidation of Solution-to-Device Feature Transfer for π-Conjugated Polymer Semiconductors28Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning39Enabling high-performance, centimeter-scale organic solar cells through three-dimensional charge transport8Interpretable machine learning for developing high-performance organic solar cells31Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy26Organic Photovoltaics’ New Renaissance: Advances Toward Roll‐to‐Roll Manufacturing of Non‐Fullerene Acceptor Organic Photovoltaics70Design for a Sustainability Approach to Organic Solar Cell Design: the Use of Machine Learning to Quantify the Trade-off between Performance, Stability, and Environmental Impact8Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing217Tailoring the Nature of Interface States in Efficient and Stable Bilayer Organic Solar Cells by a Transfer‐Printing Technique17High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective16Traps and transport resistance are the next frontiers for stable non-fullerene acceptor solar cells82Finding the FAIRness in perovskite photovoltaics research5The principles, design and applications of fused-ring electron acceptors454Renewed Prospects for Organic Photovoltaics1052Exploring the Steric Hindrance of Alkylammonium Cations in the Structural Reconfiguration of Quasi‐2D Perovskite Materials Using a High‐throughput Experimental Platform54AutoMat: Automated materials discovery for electrochemical systems16Multivariate landscapes constructed by Bayesian estimation over five hundred microbial electrochemical time profiles6Organic solar cells using oligomer acceptors for improved stability and efficiency403Novel structural feature-descriptor platform for machine learning to accelerate the development of organic photovoltaics31Reducing Voltage Losses of Organic Solar Cells against Energetics Modifications by Thermal Stress4Autonomous optimization of an organic solar cell in a 4-dimensional parameter space50Computational and data-driven modelling of solid polymer electrolytes21Cutting “lab-to-fab” short: high throughput optimization and process assessment in roll-to-roll slot die coating of printed photovoltaics27Machine learning in energy chemistry: introduction, challenges and perspectives31Machine learning integrated photocatalysis: progress and challenges94Tackling data scarcity with transfer learning: a case study of thickness characterization from optical spectra of perovskite thin films14The Application of Novel Functional Materials to Machine Learning019.28% Efficiency and Stable Polymer Solar Cells Enabled by Introducing an NIR‐Absorbing Guest Acceptor121Overcoming Disordered Preaggregation in Liquid State for Highly Efficient Organic Solar Cells Printed from Nonhalogenated Solvents50The rise of self-driving labs in chemical and materials sciences570Controlling Kinetic Quenching Depth Toward High‐Performance and Photo‐Stable Organic Solar Cells Printed from a Non‐Halogenated Solvent19Introducing a four-fold way to conceptualize artificial agency9Comprehensive Evaluation of Industrial Potentials for Indoor Organic Photovoltaic Materials via a New Figure of Merit14In Situ and Operando Characterizations of Metal Halide Perovskite and Solar Cells: Insights from Lab-Sized Devices to Upscaling Processes112Understanding Causalities in Organic Photovoltaics Device Degradation in a Machine‐Learning‐Driven High‐Throughput Platform48Challenges and opportunities of machine chemists0Mastering morphology of non-fullerene acceptors towards long-term stable organic solar cells179The role of machine learning in perovskite solar cell research42From Solution to Thin Film: Molecular Assembly of π-Conjugated Systems and Impact on (Opto)electronic Properties290Toward the Uniform of Chemical Theory, Simulation, and Experiments in Metaverse Technology9High‐Throughput Exploration of Triple‐Cation Perovskites via All‐in‐One Compositionally‐Graded Films12Transforming characterization data into information in the case of perovskite solar cells8Polymer donors with hydrophilic side-chains enabling efficient and thermally-stable polymer solar cells by non-halogenated solvent processing0Optoelectronic perovskite film characterization via machine vision7Sustainable materials acceleration platform reveals stable and efficient wide-bandgap metal halide perovskite alloys31Hexanary blends: a strategy towards thermally stable organic photovoltaics48Powering the Future: A Critical Review of Research Progress in Enhancing Stability of High‐Efficiency Organic Solar Cells95Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12Enhanced detectivity of organic photodetectors with a non-conjugated polymer additive7Data‐Driven Fine Element Tuning of Halide Double Perovskite for Enhanced Photoluminescence6MatGPT: A Vane of Materials Informatics from Past, Present, to Future74Conjugated Polymer Process Ontology and Experimental Data Repository for Organic Field-Effect Transistors9Polymer donors with hydrophilic side-chains enabling efficient and thermally-stable polymer solar cells by non-halogenated solvent processing11Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells89Step-by-Step Modulation of Crystalline Features and Exciton Kinetics for 19.2% Efficiency Ortho-Xylene Processed Organic Solar Cells87Revealing Design Rules for Improving The Photostability of Non‐Fullerene Acceptors from Molecular to Aggregation Level17Maximizing Performance and Stability of Organic Solar Cells at Low Driving Force for Charge Separation18Advantages, challenges and molecular design of different material types used in organic solar cells522Bypassing the Single Junction Limit with Advanced Photovoltaic Architectures17Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices28PANDA: a self-driving lab for studying electrodeposited polymer films27Precise control of process parameters for >23% efficiency perovskite solar cells in ambient air using an automated device acceleration platform42Advancing perovskite solar cell commercialization: Bridging materials, vacuum deposition, and AI-assisted automation32A digital twin to overcome long-time challenges in photovoltaics24Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry93Photovoltaic conversion of organic photovoltaic materials based on molecular fingerprint feature engineering0Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis16Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics64Accelerated discovery of molecular nanojunction photocatalysts for hydrogen evolution by using automated screening and flow synthesis62Machine Learning Regression Analyses of Intensity Modulation Two-Photon Microscopy (ml-IM2PM) in Perovskite Microcrystals8Guidelines for Material Design in Semitransparent Organic Solar Cells28The Future of Material Scientists in an Age of Artificial Intelligence76Wide‐Gap Perovskites for Indoor Photovoltaics23Machine Learning-Assisted Design of Advanced Polymeric Materials146Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies54Reducing Voltage Losses in Organic Photovoltaics Requires Interfacial Disorder Management15Machine learning-based screening of two-dimensional perovskite organic spacers13Using scalable computer vision to automate high-throughput semiconductor characterization20Revealing processing stability landscape of organic solar cells with automated research platforms and machine learning30Active learning concept for materials process optimization of non-fullerene organic photovoltaic using small datasets0Molecular Design and High‐Throughput Virtual Screening of Electron Donor and Non‐fullerene Acceptors for Organic Solar Cells11Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset24Integration of artificial intelligence and big data in materials science: New paradigms and scientific discoveries2Self-Driving Laboratories for Chemistry and Materials Science674Navigating the complexity of hybrid materials without structural dependency: PerovGNN as a map2Repeatable Perovskite Solar Cells through Fully Automated Spin-Coating and Quenching21Machine Learning Approaches for Predicting Power Conversion Efficiency in Organic Solar Cells: A Comprehensive Review12Machine Learning-assisted Multiobjective Optimization of Tin-based Perovskite Polycrystals1Self-assembling, narrow-bandgap rylenes for photovoltaic applications0OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler12Self-driving AMADAP laboratory: Accelerating the discovery and optimization of emerging perovskite photovoltaics16Analysis of the charge generation and recombination processes in the PM6:Y6 organic solar cell43Donor acceptor interfacial dipole polarization for efficient and stable thick-film organic photovoltaics13Enhance Photo‐Stability of Up‐Scalable Organic Solar Cells: Suppressing Radical Generation in Polymer Donors23Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells183Autonomous laboratories in China: an embodied intelligence-driven platform to accelerate chemical discovery20Bridging lab-to-fab gaps: recent advances in the reproducibility of organic solar cells4Data-driven modelling for electrolyte optimisation in dye-sensitised solar cells and photochromic solar cells10Machine Learning Applications in Polymer Informatics—An Overview1Mass-customization of organic photovoltaics and data production for machine learning models precisely predicting device behavior8Meniscus-guided coating for organic photovoltaic cells8Performance optimization of perovskite solar cells with an automated spin coating system and artificial intelligence technologies17Strategies to achieve efficiencies of over 19% for organic solar cells18Autonomous Optimization of Air‐Processed Perovskite Solar Cell in a Multidimensional Parameter Space23Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm86A High Throughput Platform to Minimize Voltage and Fill Factor Losses8Lyotropic Liquid Crystal Mediated Assembly of Donor Polymers Enhances Efficiency and Stability of Blade‐Coated Organic Solar Cells17Advancing perovskite photovoltaic technology through machine learning‐driven automation49The Value of Device Characterization for the Optimization of Organic Solar Cells2Stability of Organic Photovoltaics: From Root Causes to Advanced Analytical Techniques24Production and evaluation of high-throughput reaction data from an automated chemical synthesis platform5Predicting the glass transition temperature of norfullerene acceptor dyes by ensemble machine learning approach from their data driven approaches7Artificial Intelligence‐Driven Approaches in Semiconductor Research44AI ‐Enhanced High‐Resolution Functional Imaging Reveals Trap States and Charge Carrier Recombination Pathways in Perovskite5Material intelligence by the convergence of artificial intelligence and robotic platforms16Air‐Processed Perovskite Solar Cells: Challenges, Progress, and Industrial Strategies13Explainable Artificial Intelligence Driven Methodology for Accelerated Research of Complex Systems: Case Study of Thin‐Film Photovoltaic Kesterite‐Based Technology7Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning7Improved ZnO Post‐Treatment for High Performance Organic Solar Cell Materials5Closed-loop optimization of tetris-inspired robotic morphology evolution1Materials discovery in combinatorial and high-throughput synthesis and processing: A new Frontier for SPM9High-Throughput Molecular Design of Donors and Non-Fullerene Acceptors for Organic Solar Cells Based on Convolutional Neural Networks12Accelerating the discovery of high-efficiency donor–acceptor pairs in organic photovoltaics via SolarPCE-Net guided screening2Integration of Machine Learning and Automated Synthesis for Accelerated Drug and Material Research0AI‐Assisted Design of Advanced Polymeric Materials: Challenges and Solutions25Hybrid Learning Enables Reproducible >24% Efficiency in Autonomously Fabricated Perovskites Solar Cells7Automation of the Antisolvent Method via an Automated Spin‐Coating System and the Effects of Reduced Surface Roughness and Depth Distribution of Residual PbI 2 on Reproducibility1Cold‐Sublimating Quasi‐Solid Additive Enables High Efficiency and Long Operational Stability Binary Organic Solar Cells8Accelerating perovskite solar cell design using machine learning: a comparative study on Pb and Sn compositions0Adaptive AI decision interface for autonomous electronic material discovery10Artificial intelligence in the discovery and design of molecular semiconductors: a systematic review3Autonomous and Semi-Autonomous Laboratories in Materials Science — Conceptual Foundations and Risks: A Review Study0Generalizing organic solar cell performance and stability to untested blends: a GPR framework based on deconvolved aggregation signatures1

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