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Machine-learning prediction of facet-dependent CO coverage on Cu electrocatalysts

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DOI: 10.20517/jmi.2024.77 21 Jan 2025

Improved hardness prediction for reduced-activation high-entropy alloys by incorporating symbolic regression and domain adaptation on small datasets

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DOI: 10.20517/jmi.2024.71 9 Jan 2025

An optimized strategy for density prediction of intermetallics across varied crystal structures via graph neural network

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DOI: 10.20517/jmi.2024.76 3 Jan 2025

Interpretable model of dielectric constant for rational design of microwave dielectric materials: a machine learning study

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DOI: 10.20517/jmi.2024.75 3 Jan 2025

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

Hao Wu, Mingxuan Chen, Hao Cheng, Tong Yang, Minggang Zeng, Ming Yang

DOI: 10.20517/jmi.2024.67 3 Jan 2025

Design of Fe₂Mo@γ-GDY triatomic catalyst for electrocatalytic urea synthesis of N₂ and CO: a theoretical study

Linyuan Chi, Tonghui Wang, Qing Jiang

DOI: 10.20517/jmi.2024.49 3 Jan 2025

Unveiling defect physics in gapped metals: a theoretical investigation into defect formation and electronic structure interplay

Harshan Reddy Gopidi, Lovelesh Vashist, Oleksandr I. Malyi

DOI: 10.20517/jmi.2024.41 3 Jan 2025

Machine-learning prediction of facet-dependent CO coverage on Cu electrocatalysts

DOI: 10.20517/jmi.2024.77

Shanglin Wu, Shisheng Zheng, Wentao Zhang, Mingzheng Zhang, Shunning Li, Feng Pan

Improved hardness prediction for reduced-activation high-entropy alloys by incorporating symbolic regression and domain adaptation on small datasets

DOI: 10.20517/jmi.2024.71

Hao Pan, Mingjie Zheng, Xiaochen Li, Shijun Zhao

An optimized strategy for density prediction of intermetallics across varied crystal structures via graph neural network

DOI: 10.20517/jmi.2024.76

Dexin Zhu, Mingshuo Nie, Hong-Hui Wu, Chunlei Shang, Jiaming Zhu, Xiaoye Zhou, Yuan Zhu, Feiyang Wang, Binbin Wang, Shuize Wang, Junheng Gao, Haitao Zhao, Chaolei Zhang, Xinping Mao

Interpretable model of dielectric constant for rational design of microwave dielectric materials: a machine learning study

DOI: 10.20517/jmi.2024.75

Ye Sheng, Yabei Wu, Chang Jiang, Xiaowen Cui, Yuanqing Mao, Caichao Ye, Wenqing Zhang

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

DOI: 10.20517/jmi.2024.67

Hao Wu, Mingxuan Chen, Hao Cheng, Tong Yang, Minggang Zeng, Ming Yang

Design of Fe₂Mo@γ-GDY triatomic catalyst for electrocatalytic urea synthesis of N₂ and CO: a theoretical study

DOI: 10.20517/jmi.2024.49

Linyuan Chi, Tonghui Wang, Qing Jiang

Unveiling defect physics in gapped metals: a theoretical investigation into defect formation and electronic structure interplay

DOI: 10.20517/jmi.2024.41

Harshan Reddy Gopidi, Lovelesh Vashist, Oleksandr I. Malyi

Pre-onlines

Machine-learning prediction of facet-dependent CO coverage on Cu electrocatalysts

Improved hardness prediction for reduced-activation high-entropy alloys by incorporating symbolic regression and domain adaptation on small datasets

An optimized strategy for density prediction of intermetallics across varied crystal structures via graph neural network

Interpretable model of dielectric constant for rational design of microwave dielectric materials: a machine learning study

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

Design of Fe₂Mo@γ-GDY triatomic catalyst for electrocatalytic urea synthesis of N₂ and CO: a theoretical study

Unveiling defect physics in gapped metals: a theoretical investigation into defect formation and electronic structure interplay

Committee on Publication Ethics

Portico

Committee on Publication Ethics

Portico

Pre-onlines

Machine-learning prediction of facet-dependent CO coverage on Cu electrocatalysts

Improved hardness prediction for reduced-activation high-entropy alloys by incorporating symbolic regression and domain adaptation on small datasets

An optimized strategy for density prediction of intermetallics across varied crystal structures via graph neural network

Interpretable model of dielectric constant for rational design of microwave dielectric materials: a machine learning study

Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development

Design of Fe2Mo@γ-GDY triatomic catalyst for electrocatalytic urea synthesis of N2 and CO: a theoretical study

Unveiling defect physics in gapped metals: a theoretical investigation into defect formation and electronic structure interplay

Committee on Publication Ethics

Portico

Committee on Publication Ethics

Portico

Design of Fe₂Mo@γ-GDY triatomic catalyst for electrocatalytic urea synthesis of N₂ and CO: a theoretical study