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Microstructure-based knowledge systems for capturing process-structure evolution linkages.

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  • 1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

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|November 2, 2020
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Summary
This summary is machine-generated.

The Materials Knowledge Systems (MKS) framework now captures spatiotemporal material structure evolution, accelerating simulations significantly. This enhanced data science approach establishes crucial process-structure-property (PSP) linkages for multiscale materials modeling.

Keywords:
Cahn-Hilliard modelHomogenizationLocalizationMaterials Knowledge SystemsMultiscale modelingPhase fieldSpectral representationsStructure evolution

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Area of Science:

  • Materials Science
  • Computational Science
  • Data Science

Background:

  • Multiscale simulations are crucial for understanding material behavior.
  • Existing frameworks primarily focused on spatial linkages, limiting temporal analysis.
  • Capturing the evolution of material structure over space and time remains a challenge.

Purpose of the Study:

  • To generalize the Materials Knowledge Systems (MKS) framework for spatiotemporal multiscale simulations.
  • To incorporate diverse basis functions for enhanced process-structure-property (PSP) linkage analysis.
  • To demonstrate computational advantages over traditional numerical integration methods.

Main Methods:

  • Advancement of the MKS data science framework.
  • Introduction of generalized basis functions for MKS.
  • Application to a Cahn-Hilliard simulation for structure evolution prediction.

Main Results:

  • The generalized MKS framework successfully captures spatiotemporal material structure evolution.
  • Structure evolution was predicted three orders of magnitude faster than optimized numerical integration.
  • Demonstrated the framework's ability to learn embedded physics via influence kernels.

Conclusions:

  • The extended MKS framework provides a comprehensive approach for PSP linkages in multiscale materials modeling.
  • Offers significant computational advantages, particularly for complex simulations.
  • Represents an alternative to differential equations for understanding material physics in simulations.