A guidance framework for material design of perovskite-type electrode of SOFC based on generative model

One of the main goals of material design is to sift the proper materials with the properties we want. However, the traditional method, synthesizing and testing each material in laboratory, wastes time and energy, and the actual material we want is usually one in a million which makes it more difficult. Here, we develop a generative framework to give a guidance on material design with specific properties. Our framework is mainly drove by several variants of Generative Adversarial Networks (GANs) for material data generation. Our framework is trained with 86 perovskite-type material samples including their components information, and then we compared with various networks structures and algorithm, the result shows an acceptable accuracy of materials data generation which proved a possible method of inverse design of perovskite-type electrode of SOFC.