Materials Knowledge Systems in Python - A Data Science Framework for Accelerated Development of Hierarchical Materials
David Brough, Daniel Wheeler, Surya R. Kalidindi
There is a critical need for customized analytics that take into account the stochastic nature of materials internal structure at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open source materials data science framework that can be used to create high value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers.
Jom-Journal of the Minerals Metals & Materials Society
, Wheeler, D.
and Kalidindi, S.
Materials Knowledge Systems in Python - A Data Science Framework for Accelerated Development of Hierarchical Materials, Jom-Journal of the Minerals Metals & Materials Society
(Accessed May 18, 2021)