PyMKS is an open source, Pythonic implementation of the methodologies developed under the aegis of Materials Knowledge System (MKS) to build salient process-structure-property linkages for materials science applications. PyMKS provides for efficient tools for obtaining a digital, uniform grid representation of a materials internal structure in terms of its local states, and computing hierarchical descriptors of the structure that can be used to build efficient machine learning based mappings to the relevant response space.
The various materials data analytics workflows developed under the MKS paradigm confirm to the data transformation pipeline architecture typical to most Data Science workflows. The workflows can be boiled down to a data preprocessing step, followed by a feature generation step (fingerprinting), and a model construction step (including hyper parameter optimization). PyMKS, written in a functional programming style and supporting distributed computation (multi-core, multi-threaded, cluster), provides modular functionalities to address each of these data transformation steps, while maximally leveraging the capabilities of the underlying computing environment.
PyMKS consists of tools to compute 2-point statistics, tools for both homogenization and localization linkages, and tools for discretizing the microstructure. In addition, PyMKS has modules for generating synthetic data sets using conventional numerical simulations.
To learn about PyMKS start with the PyMKS examples, especially the introductory example. To learn more about the methods consult the technical overview for an introduction.
The two principle objects that PyMKS provides are the TwoPointCorrelation transformer and the LocalizationRegressor which provide the homogenization and localization functionality. The objects provided by PyMKS all work as either transformers or regressors in a Scikit-Learn pipeline and use both Numpy and Dask arrays for out-of-memory, distributed or parallel computations. The out-of-memory computations are still in an experimental stage as of version 0.4 and some issues still need to be resolved.
This effort has been supported with grants from NIST and the Vannevar Bush Fellowship to Professor Kalidindi at Georgia Tech.
Please submit questions and issues on the GitHub issue tracker.
To install using Conda,
$ conda install -c conda-forge pymks
To create a development environment clone this repository and run
$ conda env create -f environment.yml
$ conda activate pymks
$ python setup.py develop
in the base directory.
Install a minimal version of PyMKS with
$ pip install pymks
This is enough to run the tests, but not the examples. Some optional packages are not available via Pip. To create a development environment clone this repository and run
$ pip install .
in the base directory.