  Representing crystal structures of materials to facilitate determining them
via neural networks is crucial for enabling machine-learning applications
involving crystal structure estimation. Among these applications, the inverse
design of materials can contribute to explore materials with desired properties
without relying on luck or serendipity. We propose neural structure fields
(NeSF) as an accurate and practical approach for representing crystal
structures using neural networks. Inspired by the concepts of vector fields in
physics and implicit neural representations in computer vision, the proposed
NeSF considers a crystal structure as a continuous field rather than as a
discrete set of atoms. Unlike existing grid-based discretized spatial
representations, the NeSF overcomes the tradeoff between spatial resolution and
computational complexity and can represent any crystal structure. We propose an
autoencoder of crystal structures that can recover various crystal structures,
such as those of perovskite structure materials and cuprate superconductors.
Extensive quantitative results demonstrate the superior performance of the NeSF
compared with the existing grid-based approach.
