Structure prediction and materials design with generative neural networks

Da Yan, Adam D. Smith, Cheng-Chien Chen

Abstract: The prediction of stable crystal structures is an important part of designing solid-state crystalline materials with desired properties. Recent advances in structural feature representations and generative neural networks promise the ability to efficiently create new stable structures to use for inverse design and to search for materials with tailored functionalities.