The Materials Knowledge Materials in Python (PyMKS) framework is an object-oriented set of tools and examples, written in Python, that provide high-level access to the MKS framework for rapid creation and analysis of structure-property-processing relationships. The Materials Knowledge Systems (MKS) is a novel data science approach for solving multiscale materials science problems. It uses techniques from physics, machine learning, regression analysis, signal processing, and spatial statistics to create processing-structure-property relationships. The MKS carries the potential to bridge multiple length scales using localization and homogenization linkages, and provides a data driven framework for solving inverse material design problems.
A toolbox designed specifically for computing spatial correlations of gigantic datasets, with support for regular sized datasets as well. The toolbox takes advantage of the memory mapping functionality in MATLAB to operate on a chunk of the data at a time. The overall strategy is ineffective for parallelization as it involves tremendous overhead, but it is ideal for "sequentialization", when the algorithm needs to be able to run on a simple everyday machine.
Microstructure Maker is a a MATLAB / C++ implementation of solid texture synthesis algorithms for constructing statistically representative 3D microstructure datasets from only 2D data. This software attempts to create 3D reconstructions of microstructures from a limited number of oblique 2D sections%5Cimages. If your are studying a material system where it is difficult or impossible to apply 3D characterization techniques (X-Ray microtomography, serial sectioned SEM, etc.), then Microstructure Maker may enable you to get a 3D volumetric picture of your material system from your 2D images.
Software for the analysis of load displacement data from spherical indentation to produce stress-strain curve. The main reason for writing this code was to make the determination of the zero-point correction and indentation stress-strain curves more robust by semi-automating the analysis, developing metrics for determining appropriate answers, and providing some estimate of the uncertainty of the appropriate answer including measurements from the indentation stress-strain curve (e.g. indentation yield strength). The intended use is that the user would select a representative answer for each test and include the statistics of multiple appropriate answers for one test when determining the final answer, values, or properties.
A script designed to segment experimental micrographs into discretized local states for use in downstream P-S-P (process-structure-property) workflow (i.e. spatial correlation). This process utilizes basic built-in MATLAB functionality to perform image segmentation. The script is continuously upgraded to include added functionalities for generalized materials informatics use cases such as image pre-processing, segmentation post-processing, etc.
MATerials Innovation Network (MATIN) is an advanced cloud-based platform developed with a mission to accelerate materials innovation. MATIN is the product of the initiative and related efforts by the Institute for Materials (IMat) at Georgia Institute of Technology (Georgia Tech) in alignment with and toward implementation of the U.S. Materials Genome Initiative. Initial development and ongoing further enhancements of the MATIN platform were funded by IMat, Georgia Tech's Office of Executive Vice President for Research (EVPR) and National Institute for Standards and Technology (NIST).
We propose to design, build, and launch a modern data infrastructure to enable automated capture of all relevant metadata needed to connect all the data generated from multiple instruments by multiple users on multiple samples in a typical material research and innovation laboratory. The lack of such a supporting data infrastructure is the central bottleneck in the ongoing efforts to accelerate the rate of materials innovation and deployment in advanced technologies (see the white paper on the national Materials Genome Initiative (MGI). Our innovative solution to address this critical need involves the use of sensors and hardware devices specifically designed to capture the critical information, and instant uploading of all the data and metadata to structured hierarchical file systems in archived and shared cloud storage. This is the essential first step in allowing us to build novel knowledge systems and recommendation systems that can be mined for best practices and science automation.