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RESEARCH PAPER · The Journal of Physical Chemistry C · 2020

Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

Jiawei Zhang, P. Hu, Haifeng Wang

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Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution ReactionOriginalThe Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry3The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry6The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry2Importance learning estimator for the site-averaged turnover frequency of a disordered solid catalyst10One-step synthesis of amorphous nickel iron phosphide hierarchical nanostructures for water electrolysis with superb stability at high current density17Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis74Computational Data-Driven Materials Discovery35Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts122Machine-learning accelerated geometry optimization in molecular simulation61DFT Applications in Selected Electrocatalytic Systems0Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster: Insights from Machine Learning†20A novel cubic Ni2P/FeP electrocatalyst with cruciform surfaces for hydrogen evolution reaction5Structural Transformation of Heterogeneous Materials for Electrocatalytic Oxygen Evolution Reaction637Building a Library for Catalysts Research Using High‐Throughput Approaches45Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives151Rational Design of Graphene Derivatives for Electrochemical Reduction of Nitrogen to Ammonia76Application of density functional theory and machine learning in heterogenous-based catalytic reactions for hydrogen production72Machine Learning Utilized for the Development of Proton Exchange Membrane Electrolyzers51Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction90Data‐Driven Materials Innovation and Applications186Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects41Nickel-based nanosheets array as a binder free and highly efficient catalyst for electrochemical hydrogen evolution reaction16Designing catalysts via evolutionary-based optimization techniques18Data-driven design of electrocatalysts: principle, progress, and perspective45(2×1) Reconstruction Mechanism of Rutile TiO2(011) Surface3Saccharide concentration prediction from proxy-sea surface microlayer samples analyzed via ATR-FTIR spectroscopy and quantitative machine learning3Structural and Composition Evolution of Palladium Catalyst for CO Oxidation under Steady-State Reaction Conditions17Faster Response Chemical Reaction Using Machine Learning0Integrating Machine Learning and Molecular Simulation for Material Design and Discovery10Predicting Electronic Structure of Realistic Amorphous Surfaces4Saccharide concentration prediction from proxy sea surface microlayer samples analyzed via infrared spectroscopy and quantitative machine learning1Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective76Accelerating active catalyst discovery: a probabilistic prediction-based screening methodology with applications in dry reforming of methane4Unraveling the effect of single atom catalysts on the charging behavior of nonaqueous Mg–CO 2 batteries: a combined density functional theory and machine learning approach20Computational chemistry for water-splitting electrocatalysis238Reaction modelling of hydrogen evolution on nickel phosphide catalysts: density functional investigation8Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation238Saccharide concentration prediction from proxy sea surface microlayer samples analyzed via infrared spectroscopy and quantitative machine learning2Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning24Combinatorial high throughput methodologies: the potentials in heterogeneous catalysts synthesis, screening and discovery—a review11Saccharide Concentration Prediction from Proxy Ocean Samples Analyzed Via Infrared Spectroscopy and Quantitative Machine Learning2Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys17Identifying the Active Site on ZnxCryOz for HC-O Bond Cleavage in Syn-gas Conversion3Theoretical Calculation Assisted by Machine Learning Accelerate Optimal Electrocatalyst Finding for Hydrogen Evolution Reaction25Recent advancements in two-dimensional transition metal dichalcogenide materials towards hydrogen-evolution electrocatalysis30Platinum‐Group Metal High‐Entropy Selenides for the Hydrogen Evolution Reaction27Platinum‐Group Metal High‐Entropy Selenides for the Hydrogen Evolution Reaction2Ethylene Polymerization Activity vs Grafting Affinity Trade-off Revealed by Importance Learning Analysis of In Silico Phillips Catalyst5Phase transition and electrocatalytic properties of a 1-dimensional NiMo amorphous-crystalline alloy for the alkaline hydrogen evolution reaction13Machine learning applications on proton exchange membrane water electrolyzers: A component-level overview14The amorphous state as a frontier in computational materials design74Beyond activity: from machine learning screening to stability decoding on the study of self-supported metal phosphides in alkaline hydrogen evolution reaction4High throughput computational and experimental methods for accelerated electrochemical materials discovery27Inhibiting Overoxidation of Dynamically Evolved RuO2 to Achieve a Win–Win in Activity–Stability for Acidic Water Electrolysis77Applications of machine learning in surfaces and interfaces30Deep learning potential-assisted surface engineering for HfO2/SiO2 interface and enhanced laser damage resistance4Machine Learned Force Fields: Fundamentals, Their Reach, and Challenges1Prospects of AI in advancing green hydrogen production: From materials to applications9High-Entropy Amorphous Catalysts for Water Electrolysis: A New Frontier45Fast-Tracking Transition-State Localization via Reaction Directional Analysis2Optimizing Molecular Descriptors for Reliable Adsorption Energy Prediction on Transition Metal Nanoclusters0HiMac: High-Throughput Initialization of Multidentate Adsorption Configurations for Geometry Relaxation and Transition-State Searches0Combining DFT calculations and machine learning for rational design of hydrogen electrocatalysts2Active Learning for Optimizing Adsorption Energy Predictions in Large Chemical Spaces0

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  4. Importance learning estimator for the site-averaged turnover frequency of a disordered solid catalyst

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  5. One-step synthesis of amorphous nickel iron phosphide hierarchical nanostructures for water electrolysis with superb stability at high current density

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  11. Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster: Insights from Machine Learning†

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