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 …

Phase-field method is a density-based computational method at the mesoscale for modeling
and predicting the temporal microstructure and property evolution during materials …

Abstract The Materials Genome Initiative (MGI) advanced a new paradigm for materials
discovery and design, namely that the pace of new materials deployment could be …

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

For the past decade, considerable research effort has been devoted toward computationally
identifying and experimentally verifying single phase, high-entropy systems. However …

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 …

Although high-entropy materials are attracting considerable interest due to a combination of
useful properties and promising applications, predicting their formation remains a hindrance …

Microstructural characterization and analysis is the foundation of microstructural science,
connecting materials structure to composition, process history, and properties …