Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

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

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

Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and
materials informatics but with distinctive challenges arising from the dynamic, surface …

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

Materials informatics employs techniques, tools, and theories drawn from the emerging
fields of data science, internet, computer science and engineering, and digital technologies …

There is a critical need for customized analytics that take into account the stochastic nature
of the internal structure of materials at multiple length scales in order to extract relevant and …

Significant advances in theory, simulation tools, advanced computing infrastructure, and
experimental frameworks have enabled the field of materials science to become …

Recent works have demonstrated the viability of convolutional neural networks (CNN) for
capturing the highly non-linear microstructure-property linkages in high contrast composite …

In Computational Materials Design (CMD), it is well recognized that identifying key
microstructure characteristics is crucial for determining material design variables. However …