Pre-onlines

Advances in Graph Neural Networks for alloy design and properties predictions: a review

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DOI: 10.20517/jmi.2025.42 28 Jul 2025

MatSci-ML Studio: an interactive workflow toolkit for automated machine learning in materials science

Yu Wang, Fei Wang, Guangmao Yan, Jun Wang, Guodong Niu, Jing Feng, Jian Mao, Yan Zhao

DOI: 10.20517/jmi.2025.45 28 Jul 2025

The synergy of geometric tolerance factor and machine learning in discovering stable materials

Zhilong Wang, Fengqi You

DOI: 10.20517/jmi.2025.41 5 Aug 2025

Stacked machine learning for accurate and interpretable prediction of MXenes' work function

Lijun Shang, Yongli Yang, Yadong Yu, Pan Xiang, Li Ma, Zhonglu Guo, Mengyan Dai

DOI: 10.20517/jmi.2025.36 5 Aug 2025

Unlocking the future of materials science: key insights from the DCTMD workshop

Rika Kobayashi, Roger D. Amos, T. Daniel Crawford, Hongxia Hao, Yi Liu, Turab Lookman, Rampi Ramprasad, Matthias Scheffler, Hong Wang, Tong-Yi Zhang

DOI: 10.20517/jmi.2025.44 5 Aug 2025

Multiscale simulations of Ge-Sb-Se-Te phase-change alloys for photonic memory applications

Huiyu Li, Hanyi Zhang, Wanting Ma, Yuan Gao, Wen Zhou, Wei Zhang

DOI: 10.20517/jmi.2025.47 19 Aug 2025

Machine learning-driven new paradigm for Co-based superalloys

Jiahao Luo, Xili Liu, Qingshuang Ma, Chenghao Pei, Huiwen Yao, Jie Xiong, Qiuzhi Gao

DOI: 10.20517/jmi.2025.52 22 Aug 2025

FIND: a forward-inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning

Xuao Lu, Shiwen Luo, Jiongyang Li, Minjie Chen, Tongao Yao, Zhuoran Xu, Yujie Yan, Jun Li, Xuqiang Shao, Zhengyang Gao, Weijie Yang

DOI: 10.20517/jmi.2025.56 22 Aug 2025

Advances in Graph Neural Networks for alloy design and properties predictions: a review

DOI: 10.20517/jmi.2025.42

Zhede Zhao, Tao Hu, Shuyu Bi, Dongwei Guan, Songzhe Xu, Chaoyue Chen, Weidong Xuan, Zhongming Ren

MatSci-ML Studio: an interactive workflow toolkit for automated machine learning in materials science

DOI: 10.20517/jmi.2025.45

Yu Wang, Fei Wang, Guangmao Yan, Jun Wang, Guodong Niu, Jing Feng, Jian Mao, Yan Zhao

The synergy of geometric tolerance factor and machine learning in discovering stable materials

DOI: 10.20517/jmi.2025.41

Zhilong Wang, Fengqi You

Stacked machine learning for accurate and interpretable prediction of MXenes' work function

DOI: 10.20517/jmi.2025.36

Lijun Shang, Yongli Yang, Yadong Yu, Pan Xiang, Li Ma, Zhonglu Guo, Mengyan Dai

Unlocking the future of materials science: key insights from the DCTMD workshop

DOI: 10.20517/jmi.2025.44

Rika Kobayashi, Roger D. Amos, T. Daniel Crawford, Hongxia Hao, Yi Liu, Turab Lookman, Rampi Ramprasad, Matthias Scheffler, Hong Wang, Tong-Yi Zhang

Multiscale simulations of Ge-Sb-Se-Te phase-change alloys for photonic memory applications

DOI: 10.20517/jmi.2025.47

Huiyu Li, Hanyi Zhang, Wanting Ma, Yuan Gao, Wen Zhou, Wei Zhang

Machine learning-driven new paradigm for Co-based superalloys

DOI: 10.20517/jmi.2025.52

Jiahao Luo, Xili Liu, Qingshuang Ma, Chenghao Pei, Huiwen Yao, Jie Xiong, Qiuzhi Gao

FIND: a forward-inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning

DOI: 10.20517/jmi.2025.56

Xuao Lu, Shiwen Luo, Jiongyang Li, Minjie Chen, Tongao Yao, Zhuoran Xu, Yujie Yan, Jun Li, Xuqiang Shao, Zhengyang Gao, Weijie Yang

Pre-onlines

Advances in Graph Neural Networks for alloy design and properties predictions: a review

MatSci-ML Studio: an interactive workflow toolkit for automated machine learning in materials science

The synergy of geometric tolerance factor and machine learning in discovering stable materials

Stacked machine learning for accurate and interpretable prediction of MXenes' work function

Unlocking the future of materials science: key insights from the DCTMD workshop

Multiscale simulations of Ge-Sb-Se-Te phase-change alloys for photonic memory applications

Machine learning-driven new paradigm for Co-based superalloys

FIND: a forward-inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning

Committee on Publication Ethics

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Committee on Publication Ethics

Portico