# Design of Novel Auxetic Bi-Materials Using Convolutional Neural Networks

## TL;DR

This paper introduces a deep learning approach to design auxetic materials by predicting and optimizing their mechanical properties using a convolutional neural network.

## Contribution

The novel use of a CNN to predict and optimize auxetic bi-material microstructures for efficient metamaterial design.

## Key findings

- The CNN accurately predicts Poisson’s ratio based on material distribution in bi-material systems.
- The greedy optimization algorithm efficiently identifies auxetic microstructures using CNN inference.
- The approach offers a computationally efficient alternative to traditional finite element simulations.

## Abstract

A convolutional neural network (CNN) was developed to predict the Poisson’s ratio of representative volume elements (RVEs) composed of a bi-material system with soft and hard phases. The CNN was trained on a dataset of binary microstructure configurations, learning to approximate the effective Poisson’s ratio based on spatial material distribution. Once trained, the network was integrated into a greedy optimization algorithm to identify microstructures with auxetic behavior. The algorithm iteratively modified material arrangements, leveraging the CNN’s rapid inference to explore and refine configurations efficiently. The results demonstrate the feasibility of using deep learning for microstructure evaluation and optimization, offering a computationally efficient alternative to traditional finite element simulations. This approach provides a promising tool for the design of advanced metamaterials with tailored mechanical properties.