The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …

Interfaces such as grain boundaries (GBs) and heterointerfaces (HIs) are known to play a
crucial role in structure-property relationships of polycrystalline materials. While several …

The space of possible grain boundary structures is vast, with 5 macroscopic, crystallographic
degrees of freedom that define the character of a grain boundary. While numerous datasets …

Constructing microstructure-property-processing relationships in polycrystalline metals
remains a challenge mainly due to the lack of quantitative relations between grain boundary …

The grain boundary (GB) energy has a profound influence on the grain growth and
properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by …

We introduce the Voronoi fundamental zone (VFZ) framework which is useful for grain
boundary (GB) structure–property models and gaining insights about the nature of a five …

Correlating the bond strength with the macro strength of metals is crucial for understanding
mechanical properties and designing multi-principal-element alloys (MPEAs). Motivated by …

Steel design with the targeted properties is a challenging task due to the involvement of
many variables and their complex interactions. Artificial neural networks (ANN) recognized …

Grain boundary (GB) plays a crucial role in the mechanical properties and irradiation
resistance of nuclear materials. It is thus essential to understand and predict the defect …

Obtaining microscopic structure-property relationships for grain boundaries is challenging
due to their complex atomic structures. Recent efforts use machine learning to derive these …