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RESEARCH PAPER · ACS Applied Energy Materials · 2020

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Mohammad Rezaul Karim, Magali S. Ferrandon, Samantha Medina, Elliot Sture, Nancy N. Kariuki, Deborah J. Myers, Edward F. Holby, Piotr Zelenay, Towfiq Ahmed

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Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst SynthesisOriginalStatus and challenges for the application of platinum group metal-free catalysts in proton-exchange membrane fuel cells111Optimization of the Microwave Assisted Glycosylamines Synthesis Based on a Statistical Design of Experiments Approach9Artificial Intelligence in Chemistry: Current Trends and Future Directions286Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives151Activating γ-graphyne nanoribbons as bifunctional electrocatalysts toward oxygen reduction and hydrogen evolution reactions by edge termination and nitrogen doping29Honeycomb-like carbon with doping of a transition-metal and nitrogen for highly efficient zinc–air battery and zinc-ion battery14Targeted design of advanced electrocatalysts by machine learning110Adaptive Learning-Driven High-Throughput Synthesis of Oxygen Reduction Reaction Fe-N-C Electrocatalysts27Design of Pgm-Free Cathodic Catalyst Layers for Highly Performed Pem Fuel Cells0Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127Crystal Structures of Iron‐Based Oxides and Their Catalytic Efficiencies for the Oxygen Evolution Reaction: A Trend in Alkaline Media15Design of PGM-free cathodic catalyst layers for advanced PEM fuel cells44Application of Machine Learning in Optimizing Proton Exchange Membrane Fuel Cells: A Review171Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Development of electrochemical reactors for CO 2 electroreduction—the viability of an electrochemical CO 2 plant in Brazil2A Machine Learning Model for Adsorption Energies of Chemical Species Applied to CO 2 Electroreduction9Towards Sustainable Fuel Cells and Batteries with an AI Perspective51How machine learning can accelerate electrocatalysis discovery and optimization125Atomic metal coordinated to nitrogen-doped carbon electrocatalysts for proton exchange membrane fuel cells: a perspective on progress, pitfalls and prospectives25Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting34Machine-learning-accelerated screening of single metal atoms anchored on MnPS 3 monolayers as promising bifunctional oxygen electrocatalysts19The rational design of high-performance graphene-based single-atom electrocatalysts for the ORR using machine learning10Physics‐Based Human‐in‐the‐Loop Machine Learning Combined with Genetic Algorithm Search for Multicriteria Optimization: Electrochemical CO2 Reduction Reaction6M-N 4 -Gr/MXene Heterojunction Nanosheets as Oxygen Reduction and Evolution Reaction Catalysts: Machine Learning and Density Functional Theory Insights52Machine learning screening of high-performance single-atom electrocatalysts for two-electron oxygen reduction reaction46Machine Learning Descriptors for Data‐Driven Catalysis Study154Anion Exchange Membrane Water Electrolyzers: An Overview54Graphyne and graphdiyne nanoribbons: from their structures and properties to potential applications27Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation239Dual-stream deep learning integrated multimodal sensors for complex stimulus detection in intelligent sensory systems57Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance4Confinement Effect and Application in Catalytic Oxidation–Reduction Reaction of Confined Single-Atom Catalysts121Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts14Review of External Field Effects on Electrocatalysis: Machine Learning Guided Design32Accelerating multi-objective catalytic material design: A model-based method3Important structural parameter for curvature effect of TM-N4 embeded C70 fullerenes as electrocatalysts for CO2 reduction interpreted with machine learning and first-principles calculations3Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode16Advances in computational approaches for bridging theory and experiments in electrocatalyst design31High throughput computational and experimental methods for accelerated electrochemical materials discovery27AI-Driven Inverse Design of Materials: Past, Present, and Future78Establishing Quantitative Structure–Activity Relationships for the Degradation of Aromatic Organics by UV–H 2 O 2 Using Machine Learning4Rational Design of Carbon‐Based Electrocatalysts for H 2 O 2 Production by Machine Learning and Structural Engineering26Design and Application of Electrocatalyst Based on Machine Learning33Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Dual‐Dielectric‐Layer‐Based Iontronic Pressure Sensor Coupling Ultrahigh Sensitivity and Wide‐Range Detection for Temperature/Pressure Dual‐Mode Sensing63Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H 2 O 2 Production via Data Science19Electrosynthesis of Urea from Carbon Dioxide and Waste Nitrates: History, Recent Progress, and Future Prospects24Accelerating single-atom ORR catalyst discovery through theory and machine learning: a critical review13

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