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Add grand-canonical GB optimization (GRIP port) (#50)

* docs: spec for grand-canonical GB optimization (GRIP integration)

Vendors the GCO sampling algorithm from Chen & Frolov's GRIP
(https://github.com/enze-chen/grip) into a new physics.grand_canonical_gb
module backed by the Engine Protocol. Drops GRIP's Calculator hierarchy
and MPI launcher in favour of pwa engines + for_node across seeds.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* docs: implementation plan for grand-canonical GB optimization

Ten TDD-structured tasks (config → energy → interstitial → sampling →
slabs → bicrystal → store → workflow → integration → public API).
Each task has failing test, run-to-fail, impl, run-to-pass, commit
steps with complete code in every step. Companion to
docs/design/specs/2026-05-15-grand-canonical-gb-design.md.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(grand_canonical_gb): add GCOConfig dataclass and validator

Stubs the _grand_canonical_gb_code/ internal subpackage and lands the
sampling-parameter config + pre-loop validation. No workflow wired up
yet; just the dataclass plus 12 unit tests pinning the defaults
(match GRIP params.yaml baseline) and validator behaviour.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(grand_canonical_gb): port GB energy formula

Pure function gb_energy(E_total, n_atoms, area, E_coh) -> Egb [J/m²],
with the negative-value clamp-to-100 + warning preserved from GRIP's
Calculator.get_gb_energy. No engine dependency.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(grand_canonical_gb): port Interstitial dataclass

Direct port of GRIP's core/interstitial.py — a small holder for Voronoi
sites with from_df() and position() helpers. Behaviour preserved verbatim.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* feat(grand_canonical_gb): port sampling utilities

Ports compute_weights, sam... (continued)

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