Using large language models to probe the limits of atom-centered structural descriptors
Mapping an atomic structure to a compact set of geometric descriptors is an essential step in any machine-learning application to atomic-scale modeling. A powerful and widely-used approach can be understood as a discretization of the histogram of pair distances, triangles, etc., that results in a hierarchy of symmetry-invariant atom-centered descriptors. Unfortunately, the lower rungs on this hierarchy (two, three, four-neighbor clusters) were found to be incomplete, with symmetry-unrelated pairs of structures having exactly the same descriptors. However, all the ``degeneracies'' reported so far are resolved by considering larger clusters of neighbors to build the descriptors. We report e...
| Authors | Michelangelo Domina, Michele Ceriotti |
|---|---|
| Primary category | physics.chem-ph |
| Published | 29 July 2026 |
| Last updated at source | 2 October 2026 |
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