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 …

The explosive process of 5G communication evokes the urgent demand of miniaturized and
integrated dielectric ceramics filter. It is a pressing need to advance the development of …

We formulate a materials design strategy combining a machine learning (ML) surrogate
model with experimental design algorithms to search for high entropy alloys (HEAs) with …

Deep neural network (DNN) exhibits state-of-the-art performance in many fields including
microstructure recognition where big dataset is used in training. However, DNN trained by …

Dynamic relaxation is an intrinsic and universal feature of glasses and enables fluctuation
and dissipation to occur, which induces plentiful behaviour, maintains equilibrium, and …

The invention of bulk metallic glasses has stimulated extensive interest, due to their possible
technological applications in a variety of industrial fields and their scientific importance in …

Traditional force fields cannot model chemical reactivity, and suffer from low generality
without re-fitting. Neural network potentials promise to address these problems, offering …

Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

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 …

Optimizing two conflicting properties such as mechanical strength and toughness or
dielectric constant and breakdown strength of a material has always been a challenge. Here …