Abstract Data-driven machine learning methods have the potential to dramatically accelerate rate of materials design over conventional human-guided approaches. These would help identify or, in case generative models, even create novel crystal structures with a set specified functional properties then be synthesized or isolated laboratory. For structure generation, key bottleneck lies developing...