Pre-onlines

FT²DP: large atomic model fine-tuned machine learn-ing potential for accelerating atomistic simulation of iron-based fischer-tropsch synthesis

Zhao-Qing Liu, Zhe Deng, Huabo Zhao, Han Wang, Mohan Chen, Hong Jiang

DOI: 10.20517/jmi.2024.105 5 Mar 2025

How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?

Baoyin Yuan, Xiaohan Zhang, Chunmei Tang, Ning Wang, Siyu Ye

DOI: 10.20517/jmi.2024.106 27 Feb 2025

Multi-objective optimization in machine learning assisted materials design and discovery

Pengcheng Xu, Yingying Ma, Wencong Lu, Minjie Li, Wenyue Zhao, Zhilong Dai

DOI: 10.20517/jmi.2024.108 26 Feb 2025

Deep learning for enhanced porosity detection in AZ91 magnesium alloys using windowed perception and aggregated sensing

Minghui An, Zhiwei Zheng, Chenglin Xing, Jincheng Wang, Xuezheng Yue

DOI: 10.20517/jmi.2024.96 26 Feb 2025

Design of high energy storage ferroelectric materials by phase-field simulations

Ke Xu, Letao Yang, Jing Wang, Houbing Huang

DOI: 10.20517/jmi.2024.97 12 Feb 2025

Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation

Junwei Chen, Yixin Zhang, Jun Luan, Yunying Fan, Zhigang Yu, Bin Liu, Kuochih Chou

DOI: 10.20517/jmi.2024.89 12 Feb 2025

High-throughput screening of phosphide compounds for potassium-ion conductive cathode application

Yawen Li, Natalia A. Kabanova, Vladislav A. Blatov, Junjie Wang

DOI: 10.20517/jmi.2024.87 6 Feb 2025

FT²DP: large atomic model fine-tuned machine learn-ing potential for accelerating atomistic simulation of iron-based fischer-tropsch synthesis

DOI: 10.20517/jmi.2024.105

Zhao-Qing Liu, Zhe Deng, Huabo Zhao, Han Wang, Mohan Chen, Hong Jiang

How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?

DOI: 10.20517/jmi.2024.106

Baoyin Yuan, Xiaohan Zhang, Chunmei Tang, Ning Wang, Siyu Ye

Multi-objective optimization in machine learning assisted materials design and discovery

DOI: 10.20517/jmi.2024.108

Pengcheng Xu, Yingying Ma, Wencong Lu, Minjie Li, Wenyue Zhao, Zhilong Dai

Deep learning for enhanced porosity detection in AZ91 magnesium alloys using windowed perception and aggregated sensing

DOI: 10.20517/jmi.2024.96

Minghui An, Zhiwei Zheng, Chenglin Xing, Jincheng Wang, Xuezheng Yue

Design of high energy storage ferroelectric materials by phase-field simulations

DOI: 10.20517/jmi.2024.97

Ke Xu, Letao Yang, Jing Wang, Houbing Huang

Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation

DOI: 10.20517/jmi.2024.89

Junwei Chen, Yixin Zhang, Jun Luan, Yunying Fan, Zhigang Yu, Bin Liu, Kuochih Chou

High-throughput screening of phosphide compounds for potassium-ion conductive cathode application

DOI: 10.20517/jmi.2024.87

Yawen Li, Natalia A. Kabanova, Vladislav A. Blatov, Junjie Wang

Pre-onlines

FT²DP: large atomic model fine-tuned machine learn-ing potential for accelerating atomistic simulation of iron-based fischer-tropsch synthesis

How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?

Multi-objective optimization in machine learning assisted materials design and discovery

Deep learning for enhanced porosity detection in AZ91 magnesium alloys using windowed perception and aggregated sensing

Design of high energy storage ferroelectric materials by phase-field simulations

Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation

High-throughput screening of phosphide compounds for potassium-ion conductive cathode application

Committee on Publication Ethics

Portico

Committee on Publication Ethics

Portico

Pre-onlines

FT2DP: large atomic model fine-tuned machine learn-ing potential for accelerating atomistic simulation of iron-based fischer-tropsch synthesis

How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?

Multi-objective optimization in machine learning assisted materials design and discovery

Deep learning for enhanced porosity detection in AZ91 magnesium alloys using windowed perception and aggregated sensing

Design of high energy storage ferroelectric materials by phase-field simulations

Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation

High-throughput screening of phosphide compounds for potassium-ion conductive cathode application

Committee on Publication Ethics

Portico

Committee on Publication Ethics

Portico

FT²DP: large atomic model fine-tuned machine learn-ing potential for accelerating atomistic simulation of iron-based fischer-tropsch synthesis