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.
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 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. A short introduction to how to use PyMKS is outlined below and example cases can be found in the examples section. Both code and examples contributions are welcome.
Please feel free to ask open-ended questions about PyMKS on the pymks-general@googlegroups.com list.
There are a number of live example notebooks on Binder to help you get started with PyMKS.
The Dockerfile is for Binder, but can be used locally. See ADMINISTRATA.md for more details. The official PyMKS instance is at https://hub.docker.com/r/wd15/pymks/.