Linking properties to microstructure in liquid metal embedded elastomers via machine learning
Date
2023-08
Authors
Thoopul Anantharanga, Abhijith
Major Professor
Advisor
Sheidaei, Azadeh
Pouya, Shahram
Dayal, Vinay
Committee Member
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Abstract
Liquid metals (LM) are embedded in an elastomer matrix to obtain soft composites with
unique thermal, dielectric, and mechanical properties. They have applications in soft robotics,
biomedical engineering, and wearable electronics. By linking the structure to the properties of
these materials, it is possible to perform material design rationally. Liquid-metal embedded
elastomers (LMEEs) have been designed for targeted electro-thermo-mechanical properties by
semi-supervised learning of structure-property (SP) links in a variational autoencoder network
(VAE). The design parameters are the microstructural descriptors that are physically meaningful
and have affine relationships with the synthetization of the studied particulate composite. The
machine learning (ML) model is trained on a generated dataset of microstructural descriptors
with their multifunctional property quantities as their labels. Sobol sequence is used for in-silico
Design of Experiment (DoE) by sampling the design space to generate a comprehensive dataset of
3D microstructure realizations via a packing algorithm. The mechanical responses of the
generated microstructures are simulated using a previously developed Finite Element (FE) model,
considering the surface tension induced by LM inclusions, while the linear thermal and dielectric
constants are homogenized with the help of our in-house Fast Fourier Transform (FFT) package.
Following the training by minimization of an appropriate loss function, the VAE encoder acts as
the surrogate of numerical solvers of the multifunctional homogenizations, and its decoder is used
for the material design. Our results indicate the satisfactory performance of the surrogate model
and the inverse calculator with respect to high-fidelity numerical simulations validated with
LMEE experimental results.
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