Digital twins are emerging as powerful tools for supporting innovation as well as optimizing
the in-service performance of a broad range of complex physical machines, devices, and …

The large-scale implementation of renewable energy systems necessitates the development
of energy storage solutions to effectively manage imbalances between energy supply and …

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 …

We present an application of data analytics and supervised machine learning to allow
accurate predictions of the macroscopic stiffness and yield strength of a unidirectional …

Additive manufacturing technologies have emerged as potentially disruptive processes
whose possible impacts range across supply chain logistics, prototyping, and novel …

This paper reviews and advances a data science framework for capturing and
communicating critical information regarding the evolution of material structure in …

This paper proposes a microstructure reconstruction framework with denoising diffusion
models for the first time. The novelty and strength of the proposed model lie in its universality …

Computationally efficient structure-property (SP) linkages (ie, reduced order models) are a
necessary key ingredient in accelerating the rate of development and deployment of …

This study explores the use of supervised machine learning (ML) to predict the mechanical
properties of a family of two-phase materials using their microstructural images. Random two …

Polar skyrmions are topologically stable, swirling polarization textures with particlelike
characteristics, which hold promise for next-generation, nanoscale logic and memory …