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Communication Dans Un Congrès Année : 2023

Equivariant Message Passing Neural Network for Crystal Material Discovery

Résumé

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 EPGNN 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.
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Dates et versions

hal-04454883 , version 1 (13-02-2024)

Identifiants

Citer

Astrid Klipfel, Zied Bouraoui, Olivier Peltre, Yaël Fregier, Najwa Harrati, et al.. Equivariant Message Passing Neural Network for Crystal Material Discovery. AAAI Conference on Artificial Intelligence, Feb 2023, Washinghton DC, United States. pp.14304-14311, ⟨10.1609/aaai.v37i12.26673⟩. ⟨hal-04454883⟩
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