semantic_scholarMaterials & Design2026-08-01Cited by 0

Physics-inspired feature engineering and interpretable machine learning for thermoelectric properties of multicomponent materials

Thi H. Ho, Nguyen-Hieu Hoang, T. Trinh

View free PDFSource page

Related papers

crossrefMaterials & Design2026-05-01Cited by 2

From empirical formulas to machine learning: A comprehensive review of detonation velocity prediction for advanced energetic materials

Mohammad Hossein Keshavarz

semantic_scholarMaterials & Design2026-08-01

Integrated CALPHAD and machine learning approach for the design of high-strength Sn-In-based low-temperature solders

Hao-Wei Kuo, Yu-Chia Chang, Yu-chen Liu

View free PDFSource page

crossrefMaterials & Design

Generative Bayesian machine learning framework for inverse alloy designing of lightweight high entropy alloy with target density and experimental validation

Swati Mahato, Zequn Zhang, Simon Fellner, Behzad Sadeghi, Christoph Gammer

crossrefMaterials & Design2026-06-01Cited by 2

Inverse design of high-entropy superalloys using machine learning and generative artificial Intelligence

François Rousseau, Thierry Belmonte, Frédéric Sur, Alexandre Nominé

crossrefMaterials & Design2026-07-01Cited by 1

Machine learning in laser welding of electric vehicle batteries: Process control and quality assurance

Mingyu Wang, Lian Zhang, Yuan Liu, Pengcheng Hu, Xianzhe Peng, Zikang Xie, et al.

semantic_scholarMaterials & Design2026-08-01

A self-heating-based physics-guided neural network for fatigue life prediction of polymer matrix composites

Aravind Premanand, G. Sharma, F. Balle

View free PDFSource page