Generative adversarial networks and diffusion models in material discovery

03 May 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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.


Material Discovery
Generative Adversarial Networks
Diffusion Models
Deep Learning
Generative Models


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