Testing

The test suite lives in a flat tests/ directory and runs with pytest:

pip install -e ".[dev,gnn]"
pytest tests/

What is covered

File	Coverage
test_smoke.py	registry, molecular forces, periodic stress, a batched train step
test_neighborlist.py	periodic edge lengths within cutoff, agreement with ASE’s neighbor list, zero shifts for molecular systems
test_gnn.py	featurizer shapes and signal, triplets, rotation invariance, equivariance of every GNN model, periodic stress, upstream key translation
test_mace.py	U-matrix shapes, symmetric-contraction equivariance, flexible num_interactions (including T=0), pair repulsion, TorchScript/LAMMPS export, key translation; the ScaleShiftMACE energy expression, hand-recomputed Agnesi/Soft distance transforms, density interaction blocks, and foundation-checkpoint conversion parity with mace-torch (every architecture flavor plus multi-head slicing, built in-process; a cached-checkpoint test when one is on disk – tests never download)
test_nequip.py	hidden-irreps order, _Gate vs. e3nn’s Gate, equivariance, per-species scale/shift, export, key translation, parity with upstream nequip given identical weights
test_allegro.py	equivariance, periodic stress, export, key translation, parity with upstream allegro given identical weights
test_schnet.py	parity with an equation-by-equation reference forward built from the NIPS 2017 manuscript (SchNet is a clean-room build, so the paper — not schnetpack — is the reference), invariances, forces vs. finite differences, cosine-cutoff continuity, size extensivity, batching, TorchScript/LAMMPS export, schnetpack key translation
test_cace.py / test_physnet.py / test_ani.py / test_bamboo.py / test_les.py	the same pattern for the other faithful implementations: invariance/equivariance and parity with the upstream code given identical weights (upstream packages required where applicable), plus each model’s specific conventions
test_reaxff.py	equation-by-equation references from the ReaxFF papers (bond orders and corrections, bond energy, analytic two-atom EEM, van der Waals dimer, the water valence angle, hydrogen bonds, brute-force angle/torsion enumeration), invariances, forces vs. finite differences, size extensivity, batching, total_charge handling, trainable-group selection and training steps, ffield text and JSON library round-trips, key translation (self-contained: no third-party ReaxFF code is used – see the fidelity notes)
test_opls.py	hand-recomputed OPLS equations (harmonic bond/angle, Fourier torsion with exact 1,4 scaling, V2 impropers, the Lennard-Jones/Coulomb dimer), parity with OpenMM on randomized conformations (skipped without openmm), the relaxed ethane barrier of the 1996 paper, topology derivation counts, invariances, forces vs. finite differences, minimum-image bonded terms and periodic stress, batching, size extensivity, trainable-group selection and training steps, shared-force-field gradients, native-JSON and SEAMM .frc library readers, key translation
test_dreiding.py	hand-recomputed DREIDING equations (additive bond radii and bond-order scaling, the harmonic-cosine and linear angle forms, the Morse bond, the torsion of eq 13 with its per-bond barrier splitting, spectroscopic inversions, the Lennard-Jones and exponential-6 nonbonds with their combination rules, the Coulomb constant of eq 37 and the 12-10 hydrogen bond of eq 38), the nine torsion rules of eqs 14-23 (each branch, and symmetry under reversal), the published Tables I/II/III/V, the exact eclipsed-ethane barrier and hydrogen-bond minimum, invariances, forces vs. finite differences, minimum-image bonded terms and periodic stress, batching, size extensivity, SMARTS typing with bond-order perception, Gasteiger charges, trainable-generator selection and training steps, shared-force-field gradients, library JSON round-trips, key translation (self-contained: the LAMMPS cross-check lives in the fidelity notebook)
test_hub.py / test_ase_io.py / test_benchmark.py / test_trainer_distributed.py	dataset hub builders and caching, ASE file I/O, the benchmark runner/config, and torchrun DDP training

The equivariance tests rotate the inputs and check that energies are invariant and forces co-rotate (errors ~1e-7). The parity tests require the reference packages (the examples extra) and reproduce upstream outputs to ~1e-15/1e-16 with transplanted weights.

Conventions

• New models should get, at minimum: a registration test, an equivariance or invariance test, a periodic-stress test, and, if deployable, a script-vs-eager parity test.

• Faithful re-implementations should additionally pin down parity with the upstream code under transplanted weights, guarded by an import check so the suite still runs without the reference package installed. For a clean-room build whose reference is a manuscript rather than a code base (SchNet), pin down parity with an independent implementation of the paper’s equations instead.