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 recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

Trapped by time-consuming traditional trial-and-error methods and vast untapped
composition space, efficiently discovering novel high entropy alloys (HEAs) with exceptional …

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 …

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

The combination of multiple-principal element materials, known as high-entropy materials
(HEMs), expands the multi-dimensional compositional space to gigantic stoichiometry. It is …

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 …

Physics-informed neural networks (PINNs) have received significant attention as a unified
framework for forward, inverse, and surrogate modeling of problems governed by partial …

Metal additive manufacturing has seen extensive research and rapidly growing applications
for its high precision, efficiency, flexibility, etc. However, the appealing advantages are still …

In this study, NiTi shape memory alloys were in-situ synthesized by dual-wire-feed electron
beam additive manufacturing (dual-wire EBAM), which uses electron beam as energy …