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RESEARCH PAPER · ACS Nano · 2018

How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

Bing Cao, Lawrence Asamoah Adutwum, Anton O. Oliynyk, Erik J. Luber, Brian C. Olsen, Arthur Mar, Jillian M. Buriak

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How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic PhotovoltaicsOriginalTransforming the molecular orientation of crystalline lamellae by the degree of multi-fluorination within D–A copolymers and its effect on photovoltaic performance2Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity19Machine-Learning-Based Cyclic Voltammetry Behavior Model for Supercapacitance of Co-Doped Ceria/rGO Nanocomposite42Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors36Ternary and Quaternary Rare-Earth Transition-Metal Germanides0Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design61Data-Driven Studies of Li-Ion-Battery Materials57Controlled Design of Phase- and Size-Tunable Monodisperse Ni2P Nanoparticles in a Phosphonium-Based Ionic Liquid through Response Surface Methodology30K-Index: A Descriptor, Predictor, and Correlator of Complex Nanomorphology to Other Material Properties38New frontiers for the materials genome initiative562Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables10Physics-guided characterization and optimization of solar cells using surrogate machine learning model22Insights from Machine Learning Techniques for Predicting the Efficiency of Fullerene Derivatives‐Based Ternary Organic Solar Cells at Ternary Blend Design101Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management73Recent Advances, Design Guidelines, and Prospects of All-Polymer Solar Cells741Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures15Will organic thermoelectrics get hot?21Embedding Physics Domain Knowledge into a Bayesian Network Enables Layer-by-Layer Process Innovation for Photovoltaics4A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering104Machine Learning Optimization of p-Type Transparent Conducting Films42Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees37Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis219Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis184Accelerated Design of Catalytic Water-Cleaning Nanomotors via Machine Learning55Insights into CO2/N2 Selectivity in Porous Carbons from Deep Learning57A high throughput molecular screening for organic electronics via machine learning: present status and perspective72Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials343A robust time-dependent model of alkali-silica reaction at different temperatures24Machine Learning in Nanoscience: Big Data at Small Scales229Atom-Varied Side Chains in Conjugated Polymers Affect Efficiencies of Photovoltaic Devices Incorporating Small Molecules25Artificial intelligence: the silver bullet for sustainable materials development86Significance of thermodynamic interaction parameters in guiding the optimization of polymer:nonfullerene solar cells72Advances in Kriging-Based Autonomous X-Ray Scattering Experiments41Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics46Announcing the 2020 ACS Nano Award Lecture Laureates6Experimental-Based Optimization of Injection Molding Process Parameters for Short Product Cycle Time33Predicting structure zone diagrams for thin film synthesis by generative machine learning60Organic Photovoltaics: Relating Chemical Structure, Local Morphology, and Electronic Properties60Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization6Self-driving laboratory for accelerated discovery of thin-film materials809Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry62Phase Separation in PCDTBT:PCBM Blends: from Flory-Huggins Interaction Parameters to Ternary Phase Diagrams15Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks45Artificial Intelligence Applied to the Prediction of Organic Materials12Microfluidic‐Assisted Blade Coating of Compositional Libraries for Combinatorial Applications: The Case of Organic Photovoltaics17AI Applications through the Whole Life Cycle of Material Discovery192Design of experiments approach to luminescent CaMoO4 by atomic layer deposition8Machine learning in experimental materials chemistry66Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells102Polymorphic Metastability in Colloidal Semiconductor Nanocrystals46Increasing metabolic pathway flux by using machine learning models10Nanoscale Drug Delivery Systems for Glaucoma: Experimental and In Silico Advances18Machine learning in materials design: Algorithm and application*62Chalcophosphates: A Treasure House of Infrared Nonlinear Optical Materials75Experimental and Numerical Study Determining the Warpage Phenomenon of Thin-Wall Injection Molding17Optimization of Solar Cell Production Lines Using Neural Networks and Genetic Algorithms21Using design of experiments to understand and predict polymer microcapsule core‐shell architecture3Machine learned metaheuristic optimization of the bulk heterojunction morphology in P3HT:PCBM thin films9Machine Learning in Nano-Scale Biomedical Engineering51Engineered two-dimensional nanomaterials: an emerging paradigm for water purification and monitoring178Emerging materials intelligence ecosystems propelled by machine learning441Machine Learning Predictions of Block Copolymer Self‐Assembly59Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches42One-step statistical design of experiment for the screening and optimization of magnetite nanoparticles yields from solvothermal synthesis9Automated engineering of synthetic metabolic pathways for efficient biomanufacturing77A review on machine learning algorithms for the ionic liquid chemical space196Accelerating organic solar cell material's discovery: high-throughput screening and big data129Determining usefulness of machine learning in materials discovery using simulated research landscapes15High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68In Silico Modeling of Anticancer Drugs: Recent Advances1Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturing7Materials informatics and sustainability—The case for urgency5Using design of experiment to obtain a systematic understanding of the effect of synthesis parameters on the properties of perovskite nanocrystals15Machine learning for guiding high-temperature PEM fuel cells with greater power density30Machine Learning in FET-based Chemical and Biological Sensors: A Mini Review5Identification of the dominant recombination process for perovskite solar cells based on machine learning61Measuring, controlling and exploiting heterogeneity in optoelectronic nanowires13Experiment‐Oriented Machine Learning of Polymer:Non‐Fullerene Organic Solar Cells94Predictive AI platform on thin film evaporation in hierarchical structures15Machine Learning Techniques for Fluid Flows at the Nanoscale18Using simulation to accelerate autonomous experimentation: A case study using mechanics67Directional percolating pathways in demixing blends on a wetting substrate1Lessons learned from device modeling of organic & perovskite solar cells0Rational Design of Donor–Acceptor Based Semiconducting Copolymers with High Dielectric Constants13Machine learning and design of experiments with an application to product innovation in the chemical industry32Grafting of Acrylic Membrane Prepared from Fibers Waste for Dyes Removal: Methylene Blue and Congo Red31Research on Application of Machine Learning Technology in New Material System0A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts21Statistical Multiobjective Optimization of Thiospinel CoNi 2 S 4 Nanocrystal Synthesis via Design of Experiments28Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning57Harnessing artificial intelligence for the next generation of 3D printed medicines172Algorithmically extracted morphology descriptions for predicting device performance6An attention-driven long short-term memory network for high throughput virtual screening of organic photovoltaic candidate molecules15Machine learning in thermoelectric materials identification: Feature selection and analysis50Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches22Design and Optimization of the Antireflective Coating Properties of Silicon Solar Cells by Using Response Surface Methodology29Database Creation, Visualization, and Statistical Learning for Polymer Li+-Electrolyte Design32Optimization of the Electrospun Niobium–Tungsten Oxide Nanofibers Diameter Using Response Surface Methodology8Machine learning and computation-enabled intelligent sensor design247Noble-Metal-Free Multicomponent Nanointegration for Sustainable Energy Conversion264Pushing nanomaterials up to the kilogram scale – An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis34Autonomous experimentation systems for materials development: A community perspective398An automated domain-independent text reading, interpreting and extracting approach for reviewing the scientific literature0Capture the high-efficiency non-fullerene ternary organic solar cells formula by machine-learning-assisted energy-level alignment optimization25Machine learning-assisted optimization of TBBPA-bis-(2,3-dibromopropyl ether) extraction process from ABS polymer22Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials46Design of experiment optimization of aligned polymer thermoelectrics doped by ion-exchange26Design of Experiments for Optimization of Polyoxometalate Syntheses14New trends in nonconventional carbon dot synthesis60DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions0Flooding Prevention in Distillation and Extraction Columns with Aid of Machine Learning Approaches22Approaches using AI in medicinal chemistry2Comparison of the Surface Properties of Hydrothermally Synthesised Fe3O4@C Nanocomposites at Variable Reaction Times22DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4Experimental and analytical studies of bacterial self-healing concrete subjected to alkali-silica-reaction20DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3Effects of reinforcements on the hardness of composite seal rings2Application of Bayesian Optimization and Regression Analysis to Ferromagnetic Materials Development19Field-effect-transistor based biosensors: a review of their use in environmental monitoring applications6Computational Method‐Based Optimization of Carbon Nanotube Thin‐Film Immunosensor for Rapid Detection of SARS‐CoV‐2 Virus29Microfluidic Screening‐Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend17Optimising 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 Outlooks98Knowledge extraction in catalysis utilizing design of experiments and machine learning29DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions28Features of the educational program «Nanotechnologies and nanomaterials in energy»: machine learning0High-temperature ionic logic gates composed of an ionic rectifying solid–electrolyte interface1High-throughput and data driven strategies for the design of deep-eutectic solvent electrolytes22Lignin alkaline oxidation using reversibly-soluble bases39Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors39Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices76Machine-learning-assisted prediction of long-term performance degradation on solid oxide fuel cell cathodes induced by chromium poisoning41Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells25Automatic strain sensor design via active learning and data augmentation for soft machines101Improvement of ultralow reflectance Si solar cells by machine-learning-assisted optimization for diffusion process1Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions32Interpretable machine learning for developing high-performance organic solar cells31Recent Progress and Challenges in Plasmon‐Mediated Reduction of CO2 to Chemicals and Fuels58Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy26Data-driven design of soft sensors7Development of a fibrin-mediated gene delivery system for the treatment of cystinosis via design of experiment5Machine Learning–Assisted Design of Material Properties80Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics30Ultrasonic-assisted tetrabromobisphenol A-bis-(2,3-dibromo-2-methylpropyl ether) extraction process from ABS polymer supported by machine learning13Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing217Determining the representative features of polycystic ovary syndrome via Design of Experiments13Micro-object pose estimation with sim-to-real transfer learning using small dataset18Data-driven causal inference of process-structure relationships in nanocatalysis26Automation and Microfluidics for the Efficient, Fast, and Focused Reaction Development of Asymmetric Hydrogenation Catalysis16Activated recovery of PVC from contaminated waste extension cord-cable using a weak acid14

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  49. Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells

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