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

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

Biomimicry, adapting and implementing nature's designs provides an adequate first-order
solution to achieving superior mechanical properties. However, the design space is too vast …

The core materials knowledge needed in the accelerated design, development, and
deployment of new and improved materials is most accessible when cast in the form of …

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

Modern machine learning (ML) techniques, in conjunction with simulation-based methods,
present remarkable potential for various scientific and engineering applications. Within the …

We present a machine learning approach that integrates geometric deep learning and
Sobolev training to generate a family of finite strain anisotropic hyperelastic models that …

Identifying the key microstructure representations is crucial for computational materials
design (CMD). However, existing microstructure characterization and reconstruction (MCR) …

We apply a deep convolutional neural network segmentation model to enable novel
automated microstructure segmentation applications for complex microstructures typically …

We provide a brief discussion of “What is machine learning?” and then give a number of
examples of how these methods have recently aided the design and discovery of new …