CGWGAN: crystal generative framework based on Wyckoff generative adversarial network

Discovering novel crystals is a highly effective way to develop new materials, though it presents significant challenges. Recently, many artificial intelligence (AI) generative methods have been developed to generate new crystals. In this work, we present a crystal generative framework based on Wyckoff generative adversarial network (CGWGAN) to efficiently discover novel crystals. The CGWGAN includes three modules: a generator of crystal templates, an atom-infill module, and a crystal screening module. The generator uses a generative adversarial network (GAN) to produce crystal templates embedded with asymmetry units (ASUs), space groups, lattice vectors, and the total number of atoms within the lattice cell, ensuring that the generated templates precisely match all requirements of crystals. These templates become crystal candidates after filling in atoms of different chemical elements. These candidates are screened by M3GNet and the passed ones are subjected to density functional theory (DFT)-based calculations to finally verify their stability. As a showcase, the CGWGAN successfully discovers seven novel crystals within the Ba-Ru-O system, demonstrating its effectiveness. This work provides a knowledge-guided Artificial Intelligence generative framework for accelerating crystal discovery.