Materials machine learning (ML) is revolutionizing various areas in a fast speed, aiming to
efficiently design novel materials with superior performance. Here we reviewed the recent …

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

Soft magnetic alloys play a critical role in power conversion, magnetic sensing, magnetic
storage and electric actuating, which are fundamental components of modern technological …

With the coming of the age of artificial intelligence and big data, machine learning (ML) has
been showing powerful potentials for properties prediction of materials. For achieving …

Predicting glasses' properties from their structures is a formidable challenge because of the
inherently disordered atomic configurations. Here we tackle the problem using a new two …

High quality product quality prediction is very important for iron and steel enterprises to
ensure stable production. However, most existing prediction methods are manually …

The design of bulk metallic glasses (BMGs) via machine learning (ML) has been a topic of
active research recently. However, the prior ML models were mostly built upon supervised …

As a common thermomechanical treatment route,“cold rolling and annealing” is widely used
for the processing and grain refinement of interstitial-containing high-entropy alloys (HEAs) …

As an implementation tool of data intensive scientific research methods, machine learning
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …

Owing to its unique atomic arrangement and electronic structure, metallic glass (MG) has
been widely investigated in the field of energy storage and conversion. In the past few …