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☑ MedeA在金属合金领域的案例合集

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MedeA部分文献列表：

2024：

放射性元素回收：Observation of a promethium complex in solution

Nature，Oak Ridge National Laboratory，National Institute of Standards and Technology（NIST）

https://doi.org/10.1038/s41586-024-07267-6

甲烷干重整催化剂失活（Ni/MgAl2O4 ）：Spontaneous regeneration of active sites against catalyst deactivation

Applied Catalysis B: Environmental，清华大学，中科院物理所，苏州大学

https://doi.org/10.1016/j.apcatb.2023.123647

CO2还原：Efficient and stable CO2 to formate conversion enabled by edge-site-enriched SnS2 nanoplates

Applied Catalysis B: Environmental，深圳职业技术学院，辽宁科技大学，中国科技大学

https://doi.org/10.1016/j.apcatb.2023.123274

抗生素降解：Modulating electronic structure of active sites on iron-based nanoparticles enhances peroxymonosulfate activation

Applied Catalysis B: Environment and Energy，广东石油化工学院，湖南大学

https://doi.org/10.1016/j.apcatb.2024.124138

钙钛矿-二氧化碳还原：Isostructural phase transition-induced piezoelectricity in all-inorganic perovskite CsPbBr3 for catalytic CO2 reduction

Applied Catalysis B: Environment and Energy，暨南大学，佛山大学，浙江海洋大学，吉林大学

https://doi.org/10.1016/j.apcatb.2024.124186

陶瓷电池：Constructing highly active and durable oxygen electrode by nanoengineering for reversible protonic ceramic cell

Energy Storage Materials，河南大学，中国矿大（北京），清华大学

https://doi.org/10.1016/j.ensm.2024.103694

凝胶聚合物电解质：1 µm‐Thick Robust Gel Polymer Electrolyte with Excellent Interfacial Stability for High‐Performance Li Metal Batteries

Advanced Functional Materials，香港科技大学（广州）

https://doi.org/10.1002/adfm.202412287

电化学还原CO2：Tunable Ag-Ox coordination for industrial-level carbon-negative CO2 electrolysis

Nano Energy，南京理工大学

https://doi.org/10.1016/j.nanoen.2024.110265

光电材料：Suppressing energy migration via antiparallel spin alignment in one‐dimensional Mn2+ halide magnets with high luminescence efficiency

Angewandte Chemie，华中科技，桂林理工，华南理工

https://doi.org/10.1002/ange.202417218

电催化剂：Subnanometric Osmium Clusters Confined on Palladium Metallenes for Enhanced Hydrogen Evolution and Oxygen Reduction Catalysis

ACS Nano，浙江海洋大学

https://doi.org/10.1021/acsnano.3c10219

核材料：The mechanical properties of advanced U–Si compounds using first principles method

European Physical Journal B，中广核集团

https://doi.org/10.1140/epjb/s10051-024-00736-x

金属涂层材料：Thermo-Mechanical Properties of Interface Reaction Phases in Cr Coating on SiC Substrate: A First-Principles Investigation

SSRN，中广核集团

https://ssrn.com/abstract=4918292

金属涂层材料：Reducing the oxidation rate of Cr-coated Zr alloys under high temperature steam environment: An approach of an outer Zr coating

Journal of Nuclear Materials，上海核工院

https://doi.org/10.1016/j.jnucmat.2024.155421

机器学习：Machine-learned force fields for thermal neutron scattering law evaluations

Annals of Nuclear Energy，美国海军核实验室（NNL）

https://doi.org/10.1016/j.anucene.2024.110978

SiO2原子层沉积：Blocking mechanisms in area-selective ALD by small molecule inhibitors of different sizes: Steric shielding versus chemical passivation

Applied Surface Science，美国泛林集团（Lam Research Corporation）

https://doi.org/10.1016/j.apsusc.2024.160141

燃料电池：Investigating the Influence of the Spacer Length on Anion-Exchange Membrane Properties Using a Reactive Molecular Model

J. Phys. Chem. C，法国米其林轮胎

https://doi.org/10.1021/acs.jpcc.4c07011

热电材料：T1.2 Ceramics-Related Materials, Devices, and Processing for Heat-to-Electricity Direct Conversion and Thermal Energy Harnessing

BOOK，美国航空航天局（NASA）

https://doi.org/10.1021/acs.jpcc.4c07011

机器学习：A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning

PCCP，英国石油（BP），Materials Design

https://doi.org/10.1039/D4CP01980F

机器学习：Polyvalent Machine-Learned Potential for Cobalt: from Bulk to Nanoparticles

Physical Review Materials，巴黎萨克雷大学，名古屋大学，Materials Design

https://doi.org/10.1103/PhysRevMaterials.8.123803

机器学习：Neural network potential for Zr-H

Journal of Nuclear Materials，洛桑联邦理工学院（EPFL），Materials Design

https://doi.org/10.1016/j.jnucmat.2024.155341

机器学习：A neural-network potential for aluminum

Computational Materials Science，俄罗斯喀山联邦大学，Materials Design

https://doi.org/10.1016/j.commatsci.2024.113159

2022：

Molecular Simulations for Improved Process Modeling of an Acid Gas Removal Unit  Marianna Yiannourakou, Xavier Rozanska, Benoit Minisini, Frédérick de Meyer 

doi.org/10.1016/j.fluid.2022.113478

Computational Screening Methodology Identifies Effective Solvents for CO2 Capture  A.A. Orlov, A. Valtz, C. Coquelet, X. Rozanska, E. Wimmer, G. Marcou, D. Horvath, B. Poulain, A. Varnek, F. de Meyer

doi.org/10.1038/s42004-022-00654-y

Machine Learning for Metallurgy: A Neural-Network Potential for Zirconium

Manura Liyanage, David Reith, Volker Eyert and W. A. Curtin1

doi.org/10.1103/PhysRevMaterials.6.063804

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Aidan P. Thompson, H. Metin Aktulga, Richard Berger, Dan S.Bolintineanu, W. Michael Brown, Paul S. Crozier, Pieter J.in 't Veld, Axel Kohlmeyer, Stan G.Moore, Trung Dac Nguyen, Ray Shan, Mark J. Stevens, JulienTranchida, ChristianTrott, Steven J. Plimpton

doi.org/10.1016/j.cpc.2021.108171