The Materials Knowledge Systems (MKS) is a novel data science approach for solving multiscale materials science problems. It uses techniques from machine learning, regression analysis, signal processing, and spatial statistics to create structure-property-processing relationships. The MKS carries the potential to both bridge multiple length scales (using localization) and provide a framework for solving the inverse material design problem.

See these references for further reading:

• Computationally-Efficient Fully-Coupled Multi-Scale Modeling of Materials Phenomena Using Calibrated Localization Linkages, S. R. Kalidindi; ISRN Materials Science, vol. 2012, Article ID 305692, 2012, doi:10.5402/2012/305692.

• Formulation and Calibration of Higher-Order Elastic Localization Relationships Using the MKS Approach, Tony Fast and S. R. Kalidindi; Acta Materialia, vol. 59 (11), pp. 4595-4605, 2011, doi:10.1016/j.actamat.2011.04.005

• Developing higher-order materials knowledge systems, T. N. Fast; Thesis (PhD, Materials engineering)--Drexel University, 2011, doi:1860/4057.

The PyMKS framework is an object oriented set of tools and examples written in Python that provides high level access to the MKS method for rapid analysis of microstructure-property relationships. A description of how to use PyMKS is outlined below and example cases can be found in the examples section. Both code and example contributions are welcome.