Refractory multi-principal-element alloys (RMPEAs) exhibit high specific strength at elevated
temperatures (T). However, current RMPEAs lack a balance of room-temperature (RT) …

More than $270 billion is spent on combatting corrosion annually in the USA alone. As such,
we present a machine-learning (ML) approach to down select corrosion-resistant alloys. Our …

With the development of artificial intelligence, machine learning has a wide range of
applications in the field of materials. The sparsity of data on the mechanical properties of …

The exploration of high entropy alloys (HEAs) primarily relies on trial-and-error experiments
and multiscale modelling, which are time-consuming and resource-intensive. Recently …

As efforts associated with the exploration of multi-principal element alloys (MPEAs) using
computational and data-intensive methods continue to rise, experimental realization and …

Two data-driven machine learning (ML) models were proposed for the hardness prediction
of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs …

The coarsening of γ'precipitates in superalloys involves multiple factors and impacts the
performance of mechanical properties, such as strength and creep resistance. Classical …

This study demonstrates the use of machine learning as a potential tool to efficiently develop
new biomedical alloys with improved corrosion resistance by exploring the whole …

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their
promising high-temperature microstructural and mechanical properties. Recently, machine …

Yield strength is an important indicator of material mechanical properties, and its prediction
and evaluation are crucial for engineering design and material selection. Predicting yield …