Multi-objective generative design of three-dimensional material structures - Zhengyang Zhang; Han Fang; Zhao Xu; Jiajie Lv; Yao Shen; Yanming Wang (APL Machine Learning, 2023)
Generative design for materials has recently gained significant attention due to the rapid evolution of generative deep learning models. There have been a few successful generative design demonstrations of molecular-level structures with the help of graph neural networks. However, in the realm of macroscale material structures, most of the works are targeting two-dimensional, ungoverned structure generations. Hindered by the complexity of 3D structures, it is hard to extract customized structures with multiple desired properties from a large, unexplored design space. Here we report a novel framework, a multi-objective driven Wasserstein generative adversarial network (WGAN), to implement inverse designs of 3D structures according to given geometrical, structural, and mechanical requirements. Our framework consists of a WGAN-based network that generates 3D structures possessing geometrical and structural features learned from the target dataset. Besides, multiple objectives are introduced to our framework for the control of mechanical property and isotropy of the structures. An accurate surrogate model is incorporated into the framework to perform efficient prediction on the properties of generated structures in training iterations. With multiple objectives combined by their weight and the 3D WGAN acting as a soft constraint to regulate features that are hard to define by the traditional method, our framework has proven to be capable of tuning the properties of the generated structures in multiple aspects while keeping the selected structural features. The feasibility of a small dataset and the scalability of the objectives of other properties make our work an effective approach to provide fast and automated structure designs for various functional materials.