.. _models: Available models ================ The following pre-trained UPET models are available: .. list-table:: :header-rows: 1 :widths: 14 22 18 26 20 * - Name - Level of theory - Available sizes - To be used for - Training set * - PET-MAD-1.5 - r2SCAN - XS, S - materials & molecules (102 elements) - OMat → MAD-1.5 * - PET-OAM - PBE (Materials Project) - L, XL - materials (89 elements) - OMat → sAlex+MPtrj * - PET-OMat - PBE - XS, S, M, L, XL - materials (89 elements) - OMat * - PET-OMATPES - r2SCAN - L - materials (89 elements) - OMat → MATPES * - PET-SPICE - ωB97M-D3 - S, L - molecules (17 elements) - SPICE Recommended usage: - **PET-MAD v1.5.0** for molecular dynamics simulations of materials, surfaces, interfaces, solutions, metal complexes and other challenging systems. - **PET-OAM** for materials discovery tasks (convex hull energies, geometry optimization, phonons, etc.). - **PET-SPICE** for accurate and fast simulations of molecules and biomolecules. Model sizes ----------- The XS / S / M / L / XL suffixes correspond to a fixed family of architecture hyperparameters, introduced as the PET-OMat Pareto front in `Bigi et al., 2026 `_ (Table IV, Appendix A). The same naming convention is reused for the other UPET families (PET-MAD, PET-OMAD, PET-OMATPES, PET-SPICE), so that e.g. PET-MAD-S and PET-OMat-S share the same architectural budget. .. list-table:: :header-rows: 1 :widths: 28 12 12 12 12 12 * - Hyperparameter - XS - S - M - L - XL * - Parameter count - 4.5 M - 25.9 M - 109 M - 255 M - 730 M * - Edge feature dimension - 128 - 256 - 384 - 512 - 640 * - GNN layers - 2 - 3 - 3 - 4 - 5 * - Transformer (attention) layers - 1 - 1 - 2 - 2 - 3 * - Graph cutoff radius (Å) - 7.5 - 8.0 - 8.5 - 9.0 - 10.0 * - Adaptive neighbor number - 8 - 16 - 24 - 32 - 40 Larger sizes are more accurate but also more expensive and have a lower maximum system size before running out of GPU memory — see Fig. A2 in the reference above for the accuracy / cost / memory Pareto plot. As a rule of thumb, **S** is a good default for molecular dynamics, while **L** / **XL** are preferred for materials discovery workflows where accuracy dominates cost. .. _model-speeds: Model speeds ------------ Seeing the parameter counts of our larger models, it would be tempting to think that the models are slow. This is not the case, as we use our parameters in a very sparse manner. Here we present a few benchmarks. First, we present a benchmark of the top three open-source models on Matbench Discovery (as of Jan 14, 2026), replacing eSEN by its successor UMA-M. These were run on an H100 GPU (96GB VRAM). The structures are aluminum cells of increasing sizes and report time per energy/conservative force evaluation (lower is better). The NequIP model ran out of memory during the evaluation of the large structure (hence the missing point on the right). .. image:: /../static/images/pet-speed-comparison.png :align: center :alt: speed-oam :width: 450px Besides the large PET-OAM-XL model, the figure also shows timings for two OMat24-trained models that are smaller (and faster). To get a sense of the speed-accuracy tradeoff, as well as the memory requirements of the entire PET-OMAT family, we also show a Pareto plot comparing cost and OMat24 validation errors .. image:: /../static/images/pet-omat-pareto.png :align: center :alt: pet-omat-pareto :width: 450px Finally, we present a benchmark we ran on carbon structures using models trained on the SPICE dataset. This benchmark was run on an A100 GPU (cf. `this preprint `_ for details on the models being compared). .. image:: /../static/images/speed-spice.png :align: center :alt: speed-spice :width: 450px Uncertainty quantification -------------------------- A subset of PET-MAD checkpoints expose per-structure energy uncertainty estimates through :py:meth:`~upet.calculator.UPETCalculator.get_energy_uncertainty` and :py:meth:`~upet.calculator.UPETCalculator.get_energy_ensemble` (LLPR + shallow-ensemble heads, see :ref:`ase-uncertainty` for usage): - ``pet-mad-s`` v1.0.2 - ``pet-mad-xs`` v1.5.0 - ``pet-mad-s`` v1.5.0 Calling these methods on other checkpoints will raise an error. Non-conservative forces ----------------------- All UPET checkpoints support conservative forces (the derivative of the predicted energy). Most also expose a direct, non-conservative force head that is 2–3× faster at inference; see :ref:`ase-non-conservative`. The following checkpoints are conservative-only and therefore do **not** support ``non_conservative=True``: - ``pet-mad-s`` v1.0.2 - ``pet-spice-s`` v0.2.0 - ``pet-spice-l`` v0.2.0 PET-MAD-DOS ----------- In addition to the energy/force models above, UPET ships **PET-MAD-DOS**, a separate model family for predicting the electronic density of states, Fermi levels and bandgaps via :py:class:`~upet.calculator.PETMADDOSCalculator` (see :ref:`ase-pet-mad-dos`). .. list-table:: :header-rows: 1 :widths: 20 20 30 30 * - Version - Level of theory - Supported outputs - Notes * - 1.0 - PBE (Materials Project) - DOS, Fermi level, bandgap - Latest stable See `How et al., 2025 `_ for the methodology. Legacy models ------------- For reproducibility or to cover specific use cases, we also provide a few additional models. These are expected to have worse performance than the models above. .. list-table:: :header-rows: 1 :widths: 14 22 18 26 20 * - Name - Level of theory - Available sizes - To be used for - Training set * - PET-MAD-1 - PBESol - S - materials & molecules (85 elements) - MAD-1.0 * - PET-OMAD - PBESol - XS, S, L - materials & molecules (85 elements) - OMat → MAD-1.0 All checkpoints are available on the `HuggingFace repository `_.