Related papers: Data Pipeline Development for Grain Boundary Struc…
Most of the problems in genetic algorithms are very complex and demand a large amount of resources that current technology can not offer. Our purpose was to develop a Java-JINI distributed library that implements Genetic Algorithms with…
Tailoring the nanoscale distribution of chemical species at grain boundaries is a powerful method to dramatically influence the properties of polycrystalline materials. However, classical approaches to the problem have tacitly assumed that…
Recent advances in the numerical representation of materials opened the way for successful machine learning of grain boundary (GB) energies and the classification of GB mobility and shear coupling. Two representations were needed for these…
Understanding and controlling the properties and dynamics of topological defects is a lasting challenge in the study of two-dimensional materials, and is crucial to achieve high-quality films required for technological applications. Here…
Grain boundary plane distributions are widely used to infer the mechanisms governing grain boundary formation in polycrystalline materials. We show that such interpretations are inherently ambiguous. Using a unified eight-parameter boundary…
Studying materials informatics from a data mining perspective can be beneficial for manufacturing and other industrial engineering applications. Predictive data mining technique and machine learning algorithm are combined to design a…
Dynamic recrystallization is one of the main phenomena responsible for microstructure evolutions during hot forming. Consequently, getting a better understanding of DRX mechanisms and being able to predict them is crucial. This paper…
Data engineering pipelines are a widespread way to provide high-quality data for all kinds of data science applications. However, numerous challenges still remain in the composition and operation of such pipelines. Data engineering…
A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications,…
Grain boundaries are dominant imperfections in nanocrystalline materials that form a complex 3-dimensional (3D) network. Solute segregation to grain boundaries is strongly coupled to the grain boundary character, which governs the stability…
Polycrystalline materials have numerous applications due to their unique properties, which are often determined by the grain boundaries. Hence, quantitative characterization of grain as well as interface orientation is essential to optimize…
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 of grain boundaries have examined this space in part or…
Additively manufactured metals exhibit heterogeneous microstructure which dictates their material and failure properties. Experimental microstructural characterization techniques generate a large amount of data that requires expensive…
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of…
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from…
Systematic microstructure design requires reliable thermodynamic descriptions of each and all microstructure elements. While such descriptions are well established for most bulk phases, thermodynamic assessment of crystal defects is…
The grain boundary-mediated mechanisms that control plastic deformation of nanocrystalline metals should cause evolution of the grain boundary network, since they directly alter misorientation relationships between crystals. Unfortunately,…
Interfaces play critical roles in materials, and are usually both structurally and compositionally complex microstructural features. The precise characterization of their nature in three-dimensions at the atomic-scale is one of the grand…
As data science continues to grow in popularity, there will be an increasing need to make data science tools more scalable, flexible, and accessible. In particular, automated machine learning (AutoML) systems seek to automate the process of…
Increasingly larger number of software systems today are including data science components for descriptive, predictive, and prescriptive analytics. The collection of data science stages from acquisition, to cleaning/curation, to modeling,…