Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5451479 | Current Opinion in Solid State and Materials Science | 2017 | 12 Pages |
Abstract
This paper reviews and advances a data science framework for capturing and communicating critical information regarding the evolution of material structure in spatiotemporal multiscale simulations. This approach is called the MKS (Materials Knowledge Systems) framework, and was previously applied successfully for capturing mainly the microstructure-property linkages in spatial multiscale simulations. This paper generalizes this framework by allowing the introduction of different basis functions, and explores their potential benefits in establishing the desired process-structure-property (PSP) linkages. These new developments are demonstrated using a Cahn-Hilliard simulation as an example case study, where structure evolution was predicted three orders of magnitude faster than an optimized numerical integration algorithm. This study suggests that the MKS localization framework provides an alternate method to learn the underlying embedded physics in a numerical model expressed through Green's function based influence kernels rather than differential equations, and potentially offers significant computational advantages in problems where numerical integration schemes are challenging to optimize. With this extension, we have now established a comprehensive framework for capturing PSP linkages for multiscale materials modeling and simulations in both space and time.
Keywords
Related Topics
Physical Sciences and Engineering
Materials Science
Materials Chemistry
Authors
David B. Brough, Daniel Wheeler, James A. Warren, Surya R. Kalidindi,