Generative adversarial networks and diffusion models in material discovery

Abstract

The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal structures. In this work, we pursue three primary objectives. I) Introduce CrysTens, a crystal encoding that can be used in a wide variety of deep-learning generative models. II) Investigate and analyze the performance of Generative Adversarial Networks (GANs) and Diffusion Models to find an innovative and effective way of generating theoretical crystal structures that are synthesizable and stable. III) Show that the models that have a better “understanding” of the structure of CrysTens produce more symmetrical and realistic crystals and exhibit a better apprehension of the dataset as a whole. We accomplish these objectives using over fifty thousand Crystallographic Information Files (CIFs) from Pearson’s Crystal Database.

Inverse Design of Porous Materials: A Diffusion Model Approach

“…Furthermore, it is noteworthy that the average number of atoms comprising the unit cell of generated structures was 159.1. This value represents a significant increase compared to precedent works that utilized diffusion models to generate other crystalline materials 16,17 where the number of atoms comprising the unit cell is generally limited (e.g. fewer than 52 for CrysTens 16 and ~ 20 for CDVAE 17 ), validating ZeoDiff's ability to handle more intricate and complex structures.…”

“…This value represents a significant increase compared to precedent works that utilized diffusion models to generate other crystalline materials 16,17 where the number of atoms comprising the unit cell is generally limited (e.g. fewer than 52 for CrysTens 16 and ~ 20 for CDVAE 17 ), validating ZeoDiff's ability to handle more intricate and complex structures. Given its facility to generate new zeolite structures, we pondered on whether the model can be used to generate a new material that consists of a linear combination of two different materials.…”

“…This extension involves mapping the conventional image generation tasks based on provided text to the material generation tasks guided by specified chemical properties. As such, various classes of materials including small molecules 11,12 , proteins [13][14][15] , and crystalline materials [16][17][18] have been explored within this context. However, despite these strides, the generation of porous materials using diffusion models remains unexplored due to several challenges arising from the relatively large number of atoms, and the difficulty in capturing the essential topological features for generating porous materials.…”