2023/02/01 by Astrid Klipfel, Klipfel, Astrid, Olivier Peltre +9 · 1 citation
Computer Science · Materials Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Neural Networks and Applications #X-ray Diffraction in Crystallography
paper · pdf · doi:10.48550/arxiv.2302.00485
openalex publication_date 2023/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automatic material discovery with desired properties is a fundamental challenge for material sciences. Considerable attention has recently been devoted to generating stable crystal structures. While existing work has shown impressive success on supervised tasks such as property prediction, the progress on unsupervised tasks such as material generation is still hampered by the limited extent to which the equivalent geometric representations of the same crystal are considered. To address this challenge, we propose EMPNN a periodic equivariant message-passing neural network that learns crystal lattice deformation in an unsupervised fashion. Our model equivalently acts on lattice according to the deformation action that must be performed, making it suitable for crystal generation, relaxation and optimisation. We present experimental evaluations that demonstrate the effectiveness of our approach.