Add PolarMACE support to MaceTorchSimModel

[orionarcher]

Summary

• Wire PolarMACE-specific inputs (rcell, volume, external_field, fermi_level, density_coefficients) through the TorchSim interface with sensible defaults matching AtomicData.from_config()
• Auto-detect PolarMACE models via isinstance check (matching the ASE calculator pattern) and forward additional outputs (charges, dipole, density_coefficients, etc.) in the results dict
• Include padding support for polar tensors in the torch.compile path

Design decisions

• Auto-detection via isinstance(model, PolarMACE) — zero user burden, matches ASE calculator pattern
• Defaults match MACE's own AtomicData.from_config(): zeros for external_field, 0.0 for fermi_level, zeros(n_atoms, 1) for density_coefficients
• external_E_field read from TorchSim SimState extras (via SystemExtras.EXTERNAL_E_FIELD), mapped to external_field in MACE's data_dict
• Cell format unchanged — both MACE and PolarMACE use prepare_graph() with cell.view(-1, 3, 3), so existing [n_systems, 3, 3] format works for both

Test plan

• test_torchsim_polar_basic — forward pass with defaults (no extras)
• test_torchsim_polar_with_extras — forward pass with explicit extras
• test_torchsim_polar_no_extras_vs_zero_extras — verify defaults match zero extras
• test_torchsim_polar_batched — batched multi-system forward pass
• test_torchsim_polar_batched_with_extras — batched with per-system extras
• test_torchsim_polar_matches_ase — compare against ASE MACECalculator(model_type="PolarMACE")
• Existing test_torchsim.py tests should pass unchanged (CI verification needed)

[orionarcher]

@janosh would you mind taking a look at this? Trying to get MACE Polar working here.

[janosh]

Thanks for working on this @orionarcher!

1. polarizability and polarizability_sh are missing from per_system_keys. i think in the torch.compile padded path they'll be returned un-sliced, including padding garbage?

2. _build_polar_data calls torch.linalg.inv unconditionally on the cell, but AtomicData.from_config() guards against zero-volume cells (non-periodic systems) by falling back to zeros(3, 3). could error or produce NaN for molecules

3. density_coefficients defaults to zeros(n_atoms, 1), hardcoding the second dimension to 1. But the actual dimension depends on the model's atomic_multipoles_max_l — e.g. if max_l=1, the dimension is (1+1)^2 = 4, not 1. The ASE calculator reads this from the model (self.density_dim = (model.atomic_multipoles_max_l + 1) ** 2). i think we need do the same here to avoid a shape mismatch when a model with max_l > 0 is used.

4. isinstance(model, PolarMACE) for auto-detection is probably fine for now but could break later this year. there are plans to consolidate MACE model classes. maybe better to detect capability via a model attribute (e.g. hasattr(model, 'use_polarizability') or similar flag) rather than relying on the class hierarchy. @ilyes319 would know best

5. AtomicData.from_config() defaults total_spin to 1.0 while SimState.spin defaults to 0.0. your ASE comparison test handles this correctly, but users won't know to align them. probably worth a log warning when _is_polar and spin == 0.

6. np.random.RandomState(0) in tests is deprecated in favor of np.random.default_rng(seed=0).