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Merge branch 'main' into lammps
Signed-off-by: Shyue Ping Ong <shyuep@users.noreply.github.com>
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.github/workflows/lint.yml

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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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- uses: actions/checkout@v5
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- name: Install uv
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uses: astral-sh/setup-uv@v5
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uses: astral-sh/setup-uv@v7
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with:
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enable-cache: true
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cache-dependency-glob: "uv.lock"

.github/workflows/test.yml

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MATGL_BACKEND: "DGL"
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- uses: actions/checkout@v5
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- name: Install uv
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uses: astral-sh/setup-uv@v5
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uses: astral-sh/setup-uv@v7
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with:
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cache-dependency-glob: "uv.lock"
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uv run --no-sync pytest --cov=matgl --cov-report=xml --cov-report=term tests --color=yes
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mv .coverage coverage.dgl
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- name: Upload coverage artifacts
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uses: actions/upload-artifact@v5
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MATGL_BACKEND: "PYG"
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- name: Install uv
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uses: astral-sh/setup-uv@v5
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uses: astral-sh/setup-uv@v7
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mv .coverage coverage.pyg
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- name: Upload coverage artifacts
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if: matrix.config.optionals == 'ops'
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uses: actions/upload-artifact@v5
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needs: [test_dgl, test_pyg]
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merge-multiple: true
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# For pypi trusted publishing
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id-token: write
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steps:
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- uses: actions/checkout@v5
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- name: Set up Python 3.12
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uses: actions/setup-python@v5
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uses: actions/setup-python@v6
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- name: Install latest DGL

changes.md

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architectures; QET still deferred (charge head + LAMMPS `atom_style charge`
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wiring).
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## 3.0.1
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- PyG charge-training parity for `QET`. `PotentialLightningModule` now accepts `charge_weight` and adds a per-atom
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charge loss term (`Charge_MAE` / `Charge_RMSE`) on top of energy/force/stress; `Potential.forward` (PyG) takes
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`total_charge` / `ext_pot` and returns equilibrated charges in the output tuple, mirroring the DGL pipeline.
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- `MGLDataset` (PyG) gains `include_ref_charge` to attach per-atom `q_ref` onto each `Data` object (consumed by
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`LinearQeq`); `collate_fn_pes` (PyG) gains `include_charge` so per-atom charge labels propagate through batches.
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- Renamed PyG layer classes for cross-backend consistency: `AtomRefPyG` -> `AtomRef`, `NuclearRepulsionPyG` ->
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`NuclearRepulsion` (matching the DGL counterparts).
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- CI: bumped GitHub Actions to Node 24 versions (`actions/checkout@v5`, `actions/setup-python@v6`,
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`actions/upload-artifact@v5`, `actions/download-artifact@v5`, `astral-sh/setup-uv@v7`).
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## 3.0.0
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- **PyG `M3GNet` and `QET`.** New PyG implementations of `M3GNet` and `QET` join the existing PyG `TensorNet` and
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`MEGNet`, so all four core architectures now run on the default PyG backend without DGL.

pretrained_models/CHGNet-MPtrj-2023.12.1-2.7M-PES/README.md

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pretrained_models/CHGNet-MPtrj-2023.12.1-2.7M-PES/model.json

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pretrained_models/CHGNet-MPtrj-2024.2.13-11M-PES/README.md

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pretrained_models/CHGNet-MPtrj-2024.2.13-11M-PES/model.json

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