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

Commentary on ten selected 2025 papers in AI for Materials Science

Data-driven design of high-abundance rare earth permanent magnets for low-carbon and resource-efficient production

A federated learning–driven data fusion strategy for the hardenability prediction of gear steel

Correction: Machine learning and shap-based prediction of multifactor thermal-oxidative aging behavior in glass fiber reinforced polyamide6

LLM-driven materials knowledge extraction: multimodal parsing, ontology, and agentic systems

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

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

Deciphering the strength-ductility trade-off in (CuNiMn)-X alloys via interpretable machine learning

Transfer learning-based prediction of high-temperature fatigue life in Fe-based structural alloys with limited data

Intelligent visualization-driven materials design via two-dimensional symbolic feature generation

Phase Lab: a cloud-native CALPHAD-to-data platform for accelerated alloy design and AI/ICME/MGE workflows