Self-supervised geometric-algebra networks trained on idealized crystal data produce embeddings that separate pre-crystallization liquids better than Steinhardt order parameters, and support transfer learning between structural tasks.
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Self-Supervised Learning for Ordered Three-Dimensional Structures
Self-supervised geometric-algebra networks trained on idealized crystal data produce embeddings that separate pre-crystallization liquids better than Steinhardt order parameters, and support transfer learning between structural tasks.