High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

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 …

This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

Physics-informed neural network has strong generalization ability for small dataset, due to
the inclusion of underlying physical knowledge. Two strategies are enforced to incorporate …

A neural network model is developed to search vast compositional space of high entropy
alloys (HEAs). The model predicts the mechanical properties of HEAs better than several …

Compared to conventional alloys, multicomponent high-entropy alloys (HEAs) have
received considerable attention in recent years owing to their exceptional phase stability …

Traditional alloy design which requires deep understanding of the Process–Structure–
Property relationship, involves numerous tests and iterations. It is not cost-effective and very …

In the design and development of novel materials that have excellent mechanical properties,
classification and regression methods have been diversely used across mechanical …

Machine learning (ML) is believed to have enabled a paradigm shift in materials research,
and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of …

The solution behavior of a second element in the primary phase (α (Mg)) is important in the
design of high-performance alloys. In this work, three sets of features have been collected …