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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.