In the design and development of novel materials that have excellent mechanical properties,
classification and regression methods have been diversely used across mechanical …

Multiscale and multimodal imaging of material structures and properties provides solid
ground on which materials theory and design can flourish. Recently, KAIST announced 10 …

Abstract Electron Backscattering Diffraction (EBSD) provides important information to
discriminate phase transformation products in steels. This task is conventionally performed …

The current bulk materials discovery cycle has several inefficiencies from initial
computational predictions through fabrication and analyses. Materials are generally …

Structural materials have lagged behind other classes in the use of combinatorial and high-
throughput (CHT) methods for rapid screening and alloy development. The dual …

Deep learning is quickly becoming a standard approach to solving a range of materials
science objectives, particularly in the field of computer vision. However, labeled datasets …

Material characterization techniques are widely used to characterize the physical and
chemical properties of materials at the nanoscale and, thus, play central roles in material …

The ability to quickly analyze large imaging datasets is vital to the widespread adoption of
modern materials characterization tools, and thus the development of new materials. Image …

Recently, discriminative machine learning models have been widely used to predict various
attributes from Electron Backscatter Diffraction (EBSD) patterns. However, there has never …

New materials are a fundamental component of most major advancements in human history.
The pivotal role materials play in the development of next generation technologies has …