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

Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology.
However, its broad adoption in industry is still hindered by high entry barriers of design for …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

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 …

In metals additive manufacturing (AM), materials and components are concurrently made in
a single process as layers of metal are fabricated on top of each other in the near-final …

Machine learning tools represent key enablers for empowering material scientists and
engineers to accelerate the development of novel materials, processes and techniques. One …

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 …

The fields of machining learning and artificial intelligence are rapidly expanding, impacting
nearly every technological aspect of society. Many thousands of published manuscripts …

We propose a deep neural network (DNN) as a fast surrogate model for local stress
calculations in inhomogeneous non-linear materials. We show that the DNN predicts the …

Stochastic microstructure reconstruction has become an indispensable part of computational
materials science, but ongoing developments are specific to particular material systems. In …