CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks

Understand

Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications.

• This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data.
• Unsupervised learning methods such as Generative Adversarial Networks (GANs) can be efficiently used to produce new data.
• Cross-domain Generative Adversarial Networks were reported to achieve exciting results in image processing applications.