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RESEARCH PAPER · Chemistry of Materials · 2019

Machine Learning Optimization of p-Type Transparent Conducting Films

Lingfei Wei, Xiaojie Xu, Gurudayal Gurudayal, James Bullock, Joel W. Ager

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Machine Learning Optimization of p-Type Transparent Conducting FilmsOriginalRobust random forest based non-fullerene organic solar cells efficiency prediction71Virtual Issue on Machine-Learning Discoveries in Materials Science29Artificial intelligence: the silver bullet for sustainable materials development86Identification of host–guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study38Machine learning approach for materials technologies1Molecular insights and concepts to engineer singlet fission energy conversion devices121Bridging the p-type transparent conductive materials gap: synthesis approaches for disperse valence band materials32Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Optimized synthesis temperature and doping concentration of copper in zinc sulphide nanoparticles for green emission16Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells102Chalcogenide Perovskite YScS3 as a Potential p-Type Transparent Conducting Material5Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches42Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning57Catalyze Materials Science with Machine Learning67Machine learning-assisted optimization of TBBPA-bis-(2,3-dibromopropyl ether) extraction process from ABS polymer22Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412Machine Learning based Prediction of Wire Bonding Profile in 3D stacked integrated microelectronic packaging5Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?163Toward Precise n-Type Doping Control in MOVPE-Grown β-Ga2O3 Thin Films by Deep-Learning Approach17Ultrasonic-assisted tetrabromobisphenol A-bis-(2,3-dibromo-2-methylpropyl ether) extraction process from ABS polymer supported by machine learning13Discovery of direct band gap perovskites for light harvesting by using machine learning60Activated recovery of PVC from contaminated waste extension cord-cable using a weak acid14Machine learning supported analysis of MOVPE grown β-Ga2O3 thin films on sapphire18Machine learning enables accurate wire loop profile prediction for advanced microelectronics packaging13Machine learning for a sustainable energy future517Investigation of the aerodynamic optimization design of fluid machinery based on machine learning7Data‐Driven Approach to Tailoring Mechanical Properties of a Soft Material16Using machine learning for prediction of spray coated perovskite solar cells efficiency: From experimental to theoretical models20Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques35Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives10Dual-indicators machine learning assisted processing high-quality laser-induced fluorine-doped graphene and its application on droplet velocity monitoring sensor18Active learning concept for materials process optimization of non-fullerene organic photovoltaic using small datasets0Data‐Driven Materials Research and Development for Functional Coatings120Emerging multi-functional delafossite materials: frontier advances and prospective breakthroughs in photoelectronic applications5The effect of oxygen molecule adsorption on SrFe1-B O3 (x=0.25, 0.5, 0.75; B=Co, Mo) perovskite surface and electrochemical analysis by first-principles calculations1Empowering the future: a comprehensive review of cutting‐edge battery technologies0Artificial Intelligence‐Driven Approaches in Semiconductor Research44Achieving High-Crystallinity YSZ Thin-Films on Si Using Explainable AI and Bayesian Optimization in Resource-Constrained Settings0Inverse design of picosecond laser-induced graphene for tailorable thermal and electrical properties via SVM regression0Engineering the Peel Strength of Biobased Polycarbonate Diol-Derived Polyurethane Adhesives via Machine Learning with a Small Data Set0AI-driven battery materials innovation for AIDC energy storage1Application and modeling analysis of machine learning in semiconductor manufacturing0

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  11. Chalcogenide Perovskite YScS3 as a Potential p-Type Transparent Conducting Material

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