黄冰｜武汉大学｜学者主页

2025

电化学学科“十五五”发展趋势及优先研究领域

庄林，蔡文斌，季恒星，李箐，王功伟，辛森，赵庆，程方益，郭玉国，毛兰群，田阳，吴菲，张立敏，相艳，胡劲松，曹睿，肖丽，陶华冰，邢巍，詹东平，廖洪钢，肖梅玲，任斌，彭章泉，文锐，王翔，宋月锋，吕厚甫，夏宝玉，汪国雄，程俊，刘智攀，周敏，黄冰，李存璞，邹雨芹，王双印，林海波，魏子栋

《电化学(中英文)》

2025

Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023

Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva, Gregory Fonseca, Ilyes Batatia, N. Browning, Stefan Chmiela, Mengnan Cui, J. Frank, Stefan N. Heinen, Bing Huang, Silvan Käser, A. Kabylda, Danish Khan, Carolin Müller, Alastair J. A. Price, K. Riedmiller, Kai Töpfer, Tsz-Wai Ko, Markus Meuwly, Matthias Rupp, Gábor Csányi, O. V. von Lilienfeld, J. Margraf, Klaus-Robert Müller, Alexandre Tkatchenko

Chemical Science

精选作品

期刊论文

电化学学科“十五五”发展趋势及优先研究领域

Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023

Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

Operando Spectroscopic Insights into CO 2 Reduction at Electrode/Polyelectrolyte Interfaces

Adapting hybrid density functionals with machine learning

Discovery of highly fluorescent covalent organic frameworks through AI-assisted iterative experiment–learning cycles

Towards self-driving laboratories in chemistry and materials sciences: The central role of DFT in the era of AI

The central role of density functional theory in the AI age

Toward DMC Accuracy Across Chemical Space with Scalable Δ-QML.

Dictionary of 140k GDB and ZINC derived AMONs

Ab Initio Machine Learning in Chemical Compound Space

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

Quantum Machine Learning in Chemistry and Materials

Corrigendum: Impact of non-normal error distributions on the benchmarking and ranking of quantum machine learning models (2020 Mach. Learn.: Sci. Technol. 1 035011)

Impact of non-normal error distributions on the benchmarking and ranking of quantum machine learning models

Exploring the Composition–Activity Relation of Ni–Cu Binary Alloy Electrocatalysts for Hydrogen Oxidation Reaction in Alkaline Media

Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited.

Quantum Machine Learning in Chemical Compound Space.

The fundamentals of quantum machine learning

Alchemical and structural distribution based representation for improved QML

Unraveling the composition-activity relationship of Pt Ru binary alloy for hydrogen oxidation reaction in alkaline media

Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than DFT accuracy

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.

Machine learning prediction errors better than DFT accuracy

Chemical space exploration with molecular genes and machine learning

Efficient accurate scalable and transferable quantum machine learning with am-ons

Alchemical and structural distribution based representation for universal quantum machine learning.

The role of representation and training set selection for improved machine learning models of matter

The "DNA" of chemistry: Scalable quantum machine learning with "amons"

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity.

Systematic improvement of molecular representations for machine learning models

Spatially Resolved Quantification of the Surface Reactivity of Solid Catalysts

Pd skin on AuCu intermetallic nanoparticles: A highly active electrocatalyst for oxygen reduction reaction in alkaline media

Pt Skin on AuCu Intermetallic Substrate: A Strategy to Maximize Pt Utilization for Fuel Cells

Pt–Ru catalyzed hydrogen oxidation in alkaline media: oxophilic effect or electronic effect?

Activating Ag by even more inert Au: a peculiar effect on electrocatalysis toward oxygen reduction in alkaline media

Bond-energy decoupling: principle and application to heterogeneous catalysis

AuCu intermetallic nanoparticles: surfactant-free synthesis and novel electrochemistry

Chemical Amplitude: A Quantitative Descriptor for the Surface Reactivity of Metals

Inhibition Effect of Surface Oxygenated Species on Ammonia Oxidation Reaction

Optimization strategy for fuel-cell catalysts based on electronic effects

预印本

Autonomous mechanism discovery from minimal experiments

研究方向