# Multi-objective Generative Design of Three-Dimensional Composite Materials

## TL;DR

This paper introduces a multi-objective Wasserstein GAN framework for inverse design of 3D composite materials, enabling tailored property control while maintaining structural features, with potential for scalable, rapid, experience-free material design.

## Contribution

The paper presents a novel multi-objective Wasserstein GAN approach for inverse 3D composite structure design, integrating property prediction and feature preservation in a unified framework.

## Key findings

- Successfully generated 3D composite structures with desired properties.
- Demonstrated control over mechanical properties and isotropy.
- Effective on small datasets with potential scalability.

## Abstract

Composite materials with 3D architectures are desirable in a variety of applications for the capability of tailoring their properties to meet multiple functional requirements. By the arrangement of materials' internal components, structure design is of great significance in tuning the properties of the composites. However, most of the composite structures are proposed by empirical designs following existing patterns. Hindered by the complexity of 3D structures, it is hard to extract customized structures with multiple desired properties from large design space. Here we report a multi-objective driven Wasserstein generative adversarial network (MDWGAN) to implement inverse designs of 3D composite structures according to given geometrical, structural and mechanical requirements. Our framework consists a GAN based network which generates 3D composite structures possessing with similar geometrical and structural features to the target dataset. Besides, multiple objectives are introduced to our framework for the control of mechanical property and isotropy of the composites. Real time calculation of the properties in training iterations is achieved by an accurate surrogate model. We constructed a small and concise dataset to illustrate our framework. With multiple objectives combined by their weight, and the 3D-GAN act as a soft constraint, our framework is proved to be capable of tuning the properties of the generated composites in multiple aspects, while keeping the selected features of different kinds of structures. The feasibility on small dataset and potential scalability on objectives of other properties make our work a novel, effective approach to provide fast, experience free composite structure designs for various functional materials.