MRS Meetings and Events


DS03.10.09 2023 MRS Fall Meeting

Harnessing Structural Stochasticity in Designing Architectured Materials: Deep Generative Design and Curvature Functional-Based Design

When and Where

Dec 1, 2023
11:30am - 11:45am

Hynes, Level 2, Room 206



Hongyi Xu1,Leidong Xu1,Kiarash Naghavi Khanghah1

University of Connecticut1


Hongyi Xu1,Leidong Xu1,Kiarash Naghavi Khanghah1

University of Connecticut1
Bridging the gaps among various categories of microstructures, which include the periodic microstructures and various types of stochastic microstructure, remains a challenge in architectured material design. Traditionally, different microstructure categories require significantly different mathematical methods to define the design space. The purpose of this work is to establish a computational framework that provides a unified design space that embodies various categories of structural patterns for the design automation of mixed-category microstructures. The structural patterns include various stochastic patterns such as random particles, random fibers, random cells, spinodal structures, random amorphous structures, etc., as well as periodic structural patterns collected from metamaterial research works.<br/>First, we established a property-aware Variational Autoencoder-based deep generative design framework that embodies both stochastic and periodic 2D microstructure patterns. The proposed framework is demonstrated on two sets of design case studies: (i) searching a periodic metamaterial pattern with desired properties with a stochastic pattern as the starting point, and (ii) searching a stochastic microstructure with desired properties with a periodic pattern as the starting point.<br/>Second, we present an in-depth investigation on designing 3D mixed-category stochastic microstructures for desired properties. We establish and compare two methods, a data-driven method based on deep generative models and a mathematical method based on curvature functionals. The metrics of comparison include design performance, computational cost, scalability, interpretability of the statistical equivalency, and convenience of generating functional graded structure designs. This work is concluded with a summary of the pros and cons of each method.



Symposium Organizers

James Chapman, Boston University
Victor Fung, Georgia Institute of Technology
Prashun Gorai, National Renewable Energy Laboratory
Qian Yang, University of Connecticut

Symposium Support

Elsevier B.V.

Publishing Alliance

MRS publishes with Springer Nature