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Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials

Chemical Physics 2026-06-28 v1 Materials Science Machine Learning

Abstract

Cl(3,0)\mathrm{Cl}(3,0) interatomic potentials, despite their algebraic elegance, predict force magnitudes accurately but force directions poorly. Across ten rMD17 molecules, every L1L \leq 1 baseline in our twelve-model study attains aggregate force-cosine similarity below 0.250.25. The cause is structural. The geometric product of two vectors in R3\mathbb{R}^3 realises only the L=0L=0 and L=1L=1 components of its irreducible representation content, leaving the symmetric-traceless rank-2 component absent from the per-edge bilinear that drives each message-passing layer. We address this with CliffordSTF, which couples the Clifford multivector to closed-form symmetric-traceless tensor tracks at ranks two and three through bilinear cross-track contractions, using a single learned bilinear and no Clebsch--Gordan tables, Wigner-DD matrices, or e3nn calls. On rMD17, CliffordSTF raises aggregate force-cosine similarity from 0.0550.055 (base Clifford) to 0.5510.551, an order-of-magnitude relative directional gain, alongside improved magnitude accuracy (force MAE 15.8%15.8\% lower; energy MAE 10.9%10.9\% lower). It outperforms all CG-free or body-ordered baselines in our study (all 0.17\leq 0.17). On catalysis benchmarks, CliffordSTF achieves the best out-of-distribution S2EF energy MAE on OC22 in our experiments, and the best in-distribution energy MAE among L2L \geq 2 methods on OC22 IS2RE. An eleven-variant ablation shows the two tracks are complementary: neither alone matches the combined model.

Keywords

Cite

@article{arxiv.2606.29584,
  title  = {Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials},
  author = {Can Polat and Erchin Serpedin and Mustafa Kurban and Hasan Kurban},
  journal= {arXiv preprint arXiv:2606.29584},
  year   = {2026}
}