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Physics-informed machine learning framework integrating solid solution strengthening theory for accelerated hardness prediction in high-entropy alloys

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DOI: 10.20517/jmi.2026.20 23 Jul 2026

Physics-informed machine learning framework integrating solid solution strengthening theory for accelerated hardness prediction in high-entropy alloys

DOI: 10.20517/jmi.2026.16

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PhyMLP: an automated strategy for machine-learning potential construction via data fusion and adaptive point-sampling

DOI: 10.20517/jmi.2026.18

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DOI: 10.20517/jmi.2026.20

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Pre-onlines

Physics-informed machine learning framework integrating solid solution strengthening theory for accelerated hardness prediction in high-entropy alloys

PhyMLP: an automated strategy for machine-learning potential construction via data fusion and adaptive point-sampling

Machine-learning-enabled composition–process co-design of heat-resistant cast aluminum alloys with superior elevated-temperature strength

Committee on Publication Ethics

Portico

Committee on Publication Ethics

Portico