Microstructure-Property Tools for Structure-Property Design

Summary

Computational materials design requires a variety of tools to model processing-structure-property relationships across a range of time and length scales.  This work focuses on the development and promotion of trusted open-source tools and establish community-based best practices for structure-property linkages at the microstructure scale.

The objectives for this project include the following: 

• Improve material design efficiency by linking existing physics-based microstructure models with machine learning (ML) surrogate models.  For example, using AI to augment crystal plasticity models in the finite element code, OOF, and integrating active learning into a microstructure descriptor framework. 
• Improve and extend mesoscale modeling frameworks to facilitate their integration into the multi-scale materials design software stack.   
  • Seamless integration of a broadly used unified microstructure library into the Scikit-Learn ML paradigm 
  • Develop plug-and-play user materials descriptions to use in the OOF code as part of the crystal plasticity implementation.
  • Implement distributed microstructure descriptor into ML pipelines 
  • Integrate graph-based materials descriptor software into general frameworks.
• Improve community coherence by generating best practices and recommended software standards based on open science and FAIR data principles.
• Facilitate development of materials design platforms / apps with plug-and-play material descriptor library
• Improve development standards in the phase field community via a trusted set of benchmark problems

 

Description

Microstructure-level Structure-Property Tools

OOF: Finite Element Analysis of Microstructures enables materials scientists calculate macroscopic properties from images of real or simulated microstructures. It reads an image, assigns material properties to features in the image, and conducts virtual experiments to determine the macroscopic properties of the microstructure. More information is available here.   Current efforts are focused on integrating crystal plasticity into the code.  Small deformations have been successfully implemented into OOF.  In collaboration with CHiMaD partners, supervised and unsupervised deep learning methods are used with transfer learning principles develop an AI augmented crystal plasticity model.

 

PyMKS  A python-based framework of the Materials Knowledge System (MKS) is a data science approach for solving multiscale materials science problems using physics, machine learning, regression analysis, signal processing, and spatial statistics to create processing-structure-property relationship.  PyMKS includes tools to compute 2-point statistics, tools for both homogenization and localization linkages and tools for discretizing the microstructure.  Recent work is focused on integrating graph-based descriptors into a fully distributed python-based version of PyMKS,

 

  PFHub 

The phase field community hub is a community effort and data infrastructure to facilitate the development of benchmark problems to compare and contrast code used for solving phase field equations and to provide methods for evaluating the performance and accuracy of phase field codes.  There are currently 8 benchmarks available ranging from spinodal decomposition to homogeneous nucleation.  Semiannual workshops are held to develop and test new benchmarks and foster community engagement.