v0.1.0

[0.1.0] - 2026-07-08

Initial release. The custom-kernel-only, highest-performance build for uma-s — the per-edge
Wigner rotation runs as a custom tt-metal kernel, so ttnn comes from a source tt-metal build
that includes the op (see README "Install"); there is no slow fallback path.

Added

• Tenstorrent inference for Meta UMA (eSEN / eSCN-MD) equivariant ML interatomic potentials:
  energy, conservative analytic forces, and stress for molecules and periodic materials, behind an
  ASE calculator that mirrors fairchem's (moving off fairchem is a one-line change). Validated
  against the released uma-s-1.
• Device-resident trace loop for MD / relaxation; multi-card data-parallel throughput path.
• tt-atom verify device round-trip check and a one-command checkpoint converter.

Performance (uma-s-1, Blackhole p150a)

• Fused-rotation kernel: 4.3× vs the addcmul MAC in isolation (7.01 → 1.62 ms, PCC 0.999995);
  1.4–1.68× faster end-to-end traced MD/relax across N=54–2662, no regression at any size.
• Accuracy (vs fairchem reference): energy rel-error ≤ 5.4e-4, force PCC ≥ 0.9996 across
  molecular / periodic / slab; traced == eager (PCC 1.0). pytest 51 passed / 1 skipped.

Scope

• uma-s (lmax=mmax=2) is the supported target. Other checkpoints (e.g. uma-m) raise a clear
  error rather than silently falling back. ttnn is not a pip dependency (source build required).