Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …

Data-driven machine learning (ML) is widely employed in the analysis of materials structure–
activity relationships, performance optimization and materials design due to its superior …

Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …

The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …

This Methods/Protocols article is intended for materials scientists interested in performing
machine learning-centered research. We cover broad guidelines and best practices …

Develo** algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …

Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to
achieve desired biological responses. While there is constant evolution and innovation in …