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RESEARCH PAPER · ACS Applied Materials & Interfaces · 2020

Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches

Aaron Kirkey, Erik J. Luber, Bing Cao, Brian C. Olsen, Jillian M. Buriak

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Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning ApproachesOriginalAdvancing the Pareto front for thin-film materials using a self-driving laboratory0Advancing the Pareto front using a self-driving laboratory2Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches22Establishing relationships between bath composition and the properties of amorphous Ni–Mo alloys obtained by electrodeposition23Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials46Applied Machine Learning for Developing Next‐Generation Functional Materials72Optimising the photovoltaic parameters in donor–acceptor–acceptor ternary polymer solar cells using Machine Learning framework14Machine Learning-Assisted Development of Organic Solar Cell Materials: Issues, Analyses, and Outlooks98[Retracted] Rural Architectural Planning and Landscape Optimization Design under the Background of Ecological Environment Protection2Influence of indium tin oxide residues on the electrical performance of hydrogenated amorphous silicon thin-film transistors in the backplane of active-matrix displays4Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells25Interpretable machine learning for developing high-performance organic solar cells31A self-driving laboratory advances the Pareto front for material properties211Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Machine Learning-Assisted Polymer Design for Improving the Performance of Non-Fullerene Organic Solar Cells34Sulfone-Modified Perylene Acceptors with Improved Permittivity for Bilayer Organic Solar Cells Processed from Non-halogenated Solvents8A global evaluation model applied to wind power plant site selection46Review of roll-to-roll fabrication techniques for colloidal quantum dot solar cells372D material dispersion using a synthetic whiskey: Design of experiment optimization and preliminary application in moisture detection1Revolutionizing 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 pipeline22A machine learning prediction model for quantitative analyzing the influence of non-radiative voltage loss on non-fullerene organic solar cells41Prospective on methods of design of experiments for limited data scenarios in materials design and engineering6High‐Performance Nd: AIZO/Al2O3 Dual Active Layer Design Without Thermal Annealing: High‐Speed Electron Transport and Defect Modification in Thin Film Transistors4Self-Driving Laboratories for Chemistry and Materials Science674Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers78Mechanochemical synthesis and characterization of poly[(aniline-co-N-(4-sulfophenyl) aniline] nanofibers and its nanocomposite with titanium dioxide nanoparticles and study of their efficiency in hybrid solar cell5Combination of Transfer Learning and Chemprop Interpreter with Support of Deep Learning for the Energy Levels of Organic Photovoltaic Materials Prediction and Regulation11Machine‐Learning‐Assisted Process Optimization for High‐Performance Organic Thermoelectrics13Deep Learning‐Assisted Design of Novel Donor–Acceptor Combinations for Organic Photovoltaic Materials with Enhanced Efficiency35Fusion Modes and Number of Fused Rings: Their Impact on Acceptor–Donor–Acceptor Nonfullerene Acceptors in Organic Solar Cells2Data-driven modelling for electrolyte optimisation in dye-sensitised solar cells and photochromic solar cells10Intrinsic Reactivity Index as Descriptor for Accurately Predicting Short-Circuit Current Density in Ternary Organic Solar Cells6Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation48Bayesian Optimization of Spray Parameters for the Deposition of Ga2O3–Cu2O Heterojunctions4Artificial Intelligence‐Driven Approaches in Semiconductor Research44A Universal Machine Learning Framework Driven by Artificial Intelligence for Ion Battery Cathode Material Design31Boosting the Performance of Monolithic Dye-Sensitized Solar Cells under Simulated Solar and Artificial Light4Artificial Intelligence in Organic Photovoltaics: Predicting Power Conversion Efficiency From the Molecular Chemical Structure of (Donor/Acceptor) Pairs5Labyrinthine Microstructures with a High Dipole Moment Boron Complex for Molecular Physically Unclonable Functions3Reported synthetic conditions of carbon nitride (CNx) do not directly correlate with photocatalytic activities0Machine Learning Driven Analysis Yields Insight into the Processing Robustness of Organic Photovoltaics0

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  1. Advancing the Pareto front for thin-film materials using a self-driving laboratory

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  2. Advancing the Pareto front using a self-driving laboratory

    arXiv (Cornell University) · 2021-06-01

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  3. Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches

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  4. Establishing relationships between bath composition and the properties of amorphous Ni–Mo alloys obtained by electrodeposition

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  5. Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials

    The Journal of Physical Chemistry Letters · 2021-09-08

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  6. Applied Machine Learning for Developing Next‐Generation Functional Materials

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  7. Optimising the photovoltaic parameters in donor–acceptor–acceptor ternary polymer solar cells using Machine Learning framework

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

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  9. [Retracted] Rural Architectural Planning and Landscape Optimization Design under the Background of Ecological Environment Protection

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  10. Influence of indium tin oxide residues on the electrical performance of hydrogenated amorphous silicon thin-film transistors in the backplane of active-matrix displays

    Journal of Materials Chemistry C · 2022-01-01

    Xiang Yu, Zhiqiang Zhang, Jingxuan Pei, Jing Zhang, Rabah Boukherroub

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  11. Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells

    Journal of Materials Chemistry C · 2022-01-01

    Prateek Malhotra, Kanupriya Khandelwal, Subhayan Biswas, Fang‐Chung Chen, Ganesh Datt Sharma

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  12. Interpretable machine learning for developing high-performance organic solar cells

    Materials Today Energy · 2022-02-18

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  13. A self-driving laboratory advances the Pareto front for material properties

    Nature Communications · 2022-02-22

    Benjamin P. MacLeod, Fraser G. L. Parlane, Connor C. Rupnow, Kevan E. Dettelbach, Michael S. Elliott, Thomas D. Morrissey, Ted H. Haley, Oleksii Proskurin, Michael B. Rooney, Nina Taherimakhsousi, David Dvořák, Hsi Nien Chiu, Christopher E. B. Waizenegger, Karry Ocean, Mehrdad Mokhtari, Curtis P. Berlinguette

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

    Advanced Intelligent Systems · 2022-03-19

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

    ACS Applied Materials & Interfaces · 2022-06-13

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  16. Sulfone-Modified Perylene Acceptors with Improved Permittivity for Bilayer Organic Solar Cells Processed from Non-halogenated Solvents

    ACS Applied Energy Materials · 2023-01-31

    Peter Fürk, Matiss Reinfelds, Ilie Hanzu, Theresa Hartl, Jana B. Schaubeder, Elena Zuccalà, Heinz Wilfried Amenitsch, Thomas Rath, Gregor Trimmel

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  17. A global evaluation model applied to wind power plant site selection

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  18. Review of roll-to-roll fabrication techniques for colloidal quantum dot solar cells

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  19. 2D material dispersion using a synthetic whiskey: Design of experiment optimization and preliminary application in moisture detection

    Next Nanotechnology · 2023-03-01

    Michela Froio, Antonino Cataldo, Claudia Cencetti, Riccardo Cossi, Stefano Bellucci

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  20. Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques

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  21. Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline

    International Journal of Quantum Chemistry · 2023-08-25

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  22. A machine learning prediction model for quantitative analyzing the influence of non-radiative voltage loss on non-fullerene organic solar cells

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  23. Prospective on methods of design of experiments for limited data scenarios in materials design and engineering

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  28. Combination of Transfer Learning and Chemprop Interpreter with Support of Deep Learning for the Energy Levels of Organic Photovoltaic Materials Prediction and Regulation

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  31. Fusion Modes and Number of Fused Rings: Their Impact on Acceptor–Donor–Acceptor Nonfullerene Acceptors in Organic Solar Cells

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  41. Reported synthetic conditions of carbon nitride (CNx) do not directly correlate with photocatalytic activities

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  42. Machine Learning Driven Analysis Yields Insight into the Processing Robustness of Organic Photovoltaics

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