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Screening the activity of single-atom catalysts for the catalytic oxidation of sulfur dioxide with a kinetic activity model

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Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

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Accurate and efficient machine learning models for predicting hydrogen evolution reaction catalysts based on structural and electronic feature engineering in alloys

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Opportunities and challenges in aqueous nitrate and nitrite reduction beyond electrocatalysis

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Theoretical design principles of metal catalysts for selective ammonia oxidation from high throughput computation

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Computational chemistry for water-splitting electrocatalysis

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Mechanism of hydrogen storage on Fe3B

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Tracking the redox reaction-induced reconstruction of NiAu nanoparticles via environmental scanning transmission electron microscopy

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Effects of irradiation defects on the adsorption of oxygen on 3C-SiC low index surfaces

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Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys

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Machine Learning-Assisted High-Throughput Screening for Electrocatalytic Hydrogen Evolution Reaction

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Single-atom iron on penta-graphene assisted with non-bonding interaction as superior demercurizer: A DFT exploration

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Machine Learning Descriptors for Data‐Driven Catalysis Study

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Unraveling the role of active site Rh aggregation form in tuning the catalytic performance of ethane direct dehydrogenation

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Data-driven surrogate modeling for performance prediction and sensitivity analysis of transport properties in proton exchange membrane water electrolyzers

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Screening for lead-free inorganic double perovskites with suitable band gaps and high stability using combined machine learning and DFT calculation

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Deep dive into machine learning density functional theory for materials science and chemistry

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Machine learning utilized for the development of proton exchange membrane electrolyzers

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A descriptor for the structural stability of organic–inorganic hybrid perovskites based on binding mechanism in electronic structure

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Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

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Machine Learning Accelerated Discovery of Subnanoparticles for Electrocatalytic Hydrogen Evolution

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Single-atom iron as a promising low-temperature catalyst for selective catalytic reduction of NO with NH3: A theoretical prediction

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Highly active bimetallic Pt–Cu nanoparticles for the electrocatalysis of hydrogen evolution reactions: Experimental and theoretical insight

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Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

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Symbolic Transformer Accelerating Machine Learning Screening of Hydrogen and Deuterium Evolution Reaction Catalysts in MA2Z4 Materials

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Understanding Trends in the NO Oxidation Activity of Single‐Atom Catalysts

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Investigation of the Electrocatalytic Activity of CuRu Alloy and Its Mechanism for Hydrogen Evolution Reaction

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Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

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Machine-Learning-Driven Predictive 3D Ramified Foam Fabrication and Mechanistic Understanding

Yifei Liu and Donglei Emma Fan

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MatGPT: A Vane of Materials Informatics from Past, Present, to Future

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A catalyst family of high-entropy alloy atomic layers with square atomic arrangements comprising iron- and platinum-group metals

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Constructing 2D PtSe2/PtCo Heterojunctions by Partial Selenization for Enhanced Hydrogen Evolution

Bing Hao, Manyuan Gan, Jingjing Guo, Guoshen Li, Yanhui Song, Yongqing Shen, Bingshe Xu, Peizhi Liu and Junjie Guo

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A AgPd3/2D-CdS photocatalyst: The function of AgPd3 cocatalyst, and the enhanced photocatalytic hydrogen generation property under visible light

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Role of Dihydride and Dihydrogen Complexes in Hydrogen Evolution Reaction on Single-Atom Catalysts

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Analyzing the Active Site and Predicting the Overall Activity of Alloy Catalysts

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Theoretical Insights on Au‐based Bimetallic Alloy Electrocatalysts for Nitrogen Reduction Reaction with High Selectivity and Activity

Jun‐Lin Shi, Shi‐Qin Xiang, Dai‐Jian Su, Xiaohong Liu, Wei Zhang and Liu‐Bin Zhao

ChemSusChem, 2021, 14, 4525
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Electronegativity-oriented coordination regulation of main-group metal single-atom catalysts for oxygen reduction to H2O2: A combined study of first-principles and machine learning

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Chinese Chemical Letters, 2024, 110711
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Feature-Extended Descriptor Construction for Prediction of Consecutive Elementary Reaction Energies in Methane Oxidation

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A machine learning model with minimize feature parameters for multi-type hydrogen evolution catalyst prediction

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Identifying Stable Electrocatalysts Initialized by Data Mining: Sb2WO6 for Oxygen Reduction

Xue Jia, Zixun Yu, Fangzhou Liu, Heng Liu, Di Zhang, Egon Campos dos Santos, Hao Zheng, Yusuke Hashimoto, Yuan Chen, Li Wei and Hao Li

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Integrating Covalent Organic Framework with Transition Metal Phosphide for Noble‐Metal‐Free Visible‐Light‐Driven Photocatalytic H2 Evolution

Ge Yan, Xiaodong Sun, Kailai Zhang, Yu Zhang, Hui Li, Yuhai Dou, Ding Yuan, Hongwei Huang, Baohua Jia, Hao Li and Tianyi Ma

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Machine learning screening of high-performance single-atom electrocatalysts for two-electron oxygen reduction reaction

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Novel core-shell Ag@AgSe nanoparticle co-catalyst: In situ surface selenization for efficient photocatalytic H2 production of TiO2

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The NO Oxidation Activity of Single-Atom Catalysts

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Unraveling the Potential of Solid-State Hydrogen Storage Materials: Insights from First Principle Calculations

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Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

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Designing catalysts via evolutionary-based optimization techniques

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Complimentary Computational Cues for Water Electrocatalysis: A DFT and ML Perspective

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Machine-learning-assisted modeling of alloy ordering phenomena at the electronic scale through electronegativity

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Theoretical study of the efficiencies of graphyne supported Mo single-atom catalyst (SAC) and Mo-Ni dual-atom catalyst (DAC) on hydrogen evolution reaction

Pardis Daghooghi and Hossein Tavakol

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Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning

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Atomic Adsorption Energies Prediction on Bimetallic Transition Metal Surfaces Using an Interpretable Machine Learning‐Accelerated Density Functional Theory Approach

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Understanding the role of Ni-based single-atom alloys on the selective hydrodeoxygenation of bio-oils

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Density functional theory screening of some fundamental physical properties of Cs2InSbCl6 and Cs2InBiCl6 double perovskites

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Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects

M Karthikeyan, Durga Madhab Mahapatra, Abdul Syukor Abd Razak, Abdulaziz A.M. Abahussain, Baranitharan Ethiraj and Lakhveer Singh

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Fine-structure sensitive deep learning framework for predicting catalytic properties with high precision

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Electrochemical energy conversion and storage processes with machine learning

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Trends in Chemistry, 2024, 6, 302
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