Molecular Dynamics Metrics beta
This task evaluates how well machine-learning interatomic potentials reproduce structural, thermodynamic and vibrational observables of ab-initio molecular dynamics (AIMD) trajectories at finite temperature. Each model runs NVT simulations from the same initial structures and thermodynamic conditions as the reference first-principles trajectories. The resulting trajectories are compared via radial distribution functions (RDF), angular distribution functions (ADF), pressure distributions from the stress tensor trace, and the vibrational density of states (vDOS) obtained from the velocity autocorrelation function. Energy-fluctuation and force RMSEs are shown as maintainer-computed private-label diagnostics when available, but they are excluded from CMDS.
⚠️ Interpret with caution. The molecular dynamics task is preliminary. The DynaMat v1.0 reference test set and the metrics are still evolving. Treat the current MD metrics and ranking as indicative only, expect changes as test set and metrics evolve.
The reference set currently holds 17 structures (DynaMat v1.0, spanning pure metals, alloys, high-entropy alloys, transition-metal dichalcogenides, perovskites and molecular crystals at 293–1500 K); an upcoming v2 release will grow this AIMD test set to ~100 structures. Collabs to grow it even further welcome! The public reference data intentionally omits energies and forces. Energy/force RMSEs shown here are maintainer-computed private-label diagnostics and are excluded from CMDS, which ranks trajectory-level observables plus speed. All models currently on the leaderboard were run through a unified script,
models/run_md.py. If your model isn’t listed, we invite you to run it and submit your metrics via PR.For details on the MD modeling task, the DynaMat reference set and the CMDS metric, refer to arXiv:2607.03433.
| # | Model | ΔERMSE | FRMSE | ΔADF | ΔvDOS | PMAE | PW1 | ΔP | CMDS ↑ | Speed | Slowdown | Params | Targets | Date Added | Links | rcut | Training Set | Org |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | GRACE-2L-OAM-L | 0.7 | 75.9 | 1.4 | 22.8 | 0.72 | 0.72 | 43.1 | 0.759 | 18.4k | 1.97 | 26.4M | EFSG | 2025-09-09 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex |  | |
| 2 | GRACE-2L-OAM | 0.8 | 89.3 | 1.4 | 23.3 | 0.80 | 0.80 | 50.3 | 0.751 | 14k | 1.49 | 12.6M | EFSG | 2025-02-06 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex | | |
| 3 | ORB v2 MPA | 1.3 | 102.4 | 1.6 | 24.0 | 0.88 | 0.89 | 57.4 | 0.750 | 9.38k | 1 | 25.2M | EFSD | 2024-10-11 | 10 Å | 3.25M (32.1M) MPtrj+Alex |  | |
| 4 | GRACE-1L-OAM | 1.1 | 121.5 | 1.5 | 24.8 | 0.76 | 0.78 | 52.9 | 0.749 | 11.7k | 1.24 | 3.45M | EFSG | 2025-02-06 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex | | |
| 5 | GRACE-3L-OAM-L | 0.5 | 54.2 | 1.3 | 22.0 | 0.80 | 0.81 | 43.2 | 0.739 | 27.2k | 2.9 | 42.1M | EFSG | 2026-07-02 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex | | |
| 6 | ORB v2 MPtrj | 1.9 | 115.5 | 2.5 | 23.2 | 0.93 | 0.95 | 65.2 | 0.727 | 9.44k | 1.01 | 25.2M | EFSD | 2024-10-14 | 10 Å | 146k (1.58M) MPtrj | | |
| 7 | GRACE-2L-MPtrj | 1.4 | 148.0 | 2.1 | 25.2 | 0.78 | 0.79 | 64.6 | 0.698 | 14.8k | 1.58 | 15.3M | EFSG | 2024-11-21 | 6 Å | 146k (1.58M) MPtrj | | |
| 8 | Nequip-OAM-L | 0.7 | 74.9 | 1.5 | 22.4 | 0.82 | 0.82 | 49.1 | 0.670 | 65.1k | 6.94 | 9.6M | EFSG | 2025-09-08 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex |  | |
| 9 | MACE-MPA-0 | 1.1 | 109.7 | 1.5 | 22.8 | 1.21 | 1.21 | 65.8 | 0.659 | 31.8k | 3.39 | 9.06M | EFSG | 2024-12-09 | 6 Å | 3.37M (12M) MPtrj+sAlex |  | |
| 10 | MatterSim v1 5M | 1.2 | 112.7 | 2.0 | 23.0 | 0.93 | 0.95 | 64.3 | 0.659 | 33.7k | 3.59 | 4.55M | EFSG | 2024-12-16 | 5 Å | 17M MatterSim | ||
| 11 | HIENet | 1.2 | 118.9 | 2.3 | 24.4 | 0.72 | 0.73 | 47.6 | 0.653 | 82.6k | 8.81 | 7.51M | EFSG | 2025-07-01 | 5 Å | 146k (1.58M) MPtrj |  | |
| 12 | AlphaNet-v1-OAM* | 1.3 | 142.3 | 2.2 | 28.1 | 0.97 | 0.97 | 61.1 | 0.653 | 33.8k | 3.6 | 4.65M | EFSG | 2025-05-12 | 5 Å | 6.6M (113M) MPtrj+OMat24+sAlex |      | |
| 13 | SevenNet-l3i5* | 1.2 | 123.3 | 2.1 | 24.8 | 0.81 | 0.82 | 56.1 | 0.651 | 54.4k | 5.79 | 1.17M | EFSG | 2024-12-10 | 5 Å | 146k (1.58M) MPtrj |  | |
| 14 | DPA-3.1-3M-FT | 0.7 | 68.9 | 1.3 | 22.4 | 0.75 | 0.76 | 50.1 | 0.647 | 93.1k | 9.93 | 3.27M | EFSG | 2025-06-05 | 6 Å | 163M OpenLAM |   | |
| 15 | Eqnorm MPtrj | 1.3 | 125.0 | 2.6 | 25.7 | 0.82 | 0.83 | 56.6 | 0.639 | 61.2k | 6.53 | 1.31M | EFSG | 2025-05-26 | 6 Å | 146k (1.58M) MPtrj |  | |
| 16 | PET-OAM-XL | 0.5 | 43.3 | 1.3 | 21.7 | 0.72 | 0.74 | 46.1 | 0.634 | 149k | 15.85 | 730M | EFSG | 2026-01-10 | n/a | 6.6M (113M) MPtrj+OMat24+sAlex |  | |
| 17 | MACE-MP-0 | 1.7 | 165.7 | 3.3 | 31.2 | 0.80 | 0.82 | 66.4 | 0.633 | 29.4k | 3.13 | 4.69M | EFSG | 2023-07-14 | 6 Å | 146k (1.58M) MPtrj | | |
| 18 | Nequip-MP-L | 1.2 | 116.8 | 2.2 | 24.4 | 0.92 | 0.92 | 60.1 | 0.629 | 65.2k | 6.95 | 9.6M | EFSG | 2025-09-08 | 6 Å | 146k (1.58M) MPtrj | | |
| 19 | SevenNet-Omni-i12* | 0.5 | 47.2 | 1.4 | 21.8 | 0.76 | 0.77 | 45.2 | 0.626 | 178k | 19 | 54.9M | EFSG | 2026-01-12 | 6 Å | 243M COSMOSDataset | | |
| 20 | TACE-OAM-L | 0.5 | 60.1 | 1.3 | 22.5 | 0.75 | 0.75 | 42.6 | 0.625 | 199k | 21.23 | 82.9M | EFSG | 2026-04-09 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex |  | |
| 21 | eSEN-30M-OAM | 0.5 | 50.8 | 1.3 | 21.8 | 0.79 | 0.81 | 42.2 | 0.625 | 213k | 22.73 | 30.2M | EFSG | 2025-03-17 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex | ||
| 22 | Nequip-OAM-XL | 0.6 | 62.9 | 1.4 | 22.2 | 0.88 | 0.88 | 50.0 | 0.623 | 141k | 15.07 | 32.1M | EFSG | 2025-11-30 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex | | |
| 23 | Nequix MP PFT | 1.4 | 131.1 | 2.8 | 27.5 | 0.79 | 0.80 | 69.5 | 0.616 | 41.4k | 4.42 | 708k | EFSHG | 2026-01-08 | 6 Å | 154k (1.59M) MPtrj+MDR-MP PBE ωq |   | |
| 24 | eqV2 S DeNS | 6.1 | 110.2 | 2.2 | 26.5 | 0.97 | 0.99 | 56.0 | 0.615 | 93.2k | 9.93 | 31.2M | EFSD | 2024-10-18 | 12 Å | 146k (1.58M) MPtrj | ||
| 25 | Nequix MP | 1.4 | 136.1 | 2.7 | 27.7 | 0.96 | 0.96 | 73.8 | 0.603 | 41.3k | 4.4 | 708k | EFSG | 2025-08-17 | 6 Å | 146k (1.58M) MPtrj | | |
| 26 | Allegro-OAM-L | 0.7 | 84.8 | 1.4 | 23.2 | 0.78 | 0.80 | 47.1 | 0.602 | 228k | 24.34 | 9.7M | EFSG | 2025-09-08 | 7 Å | 6.6M (113M) MPtrj+OMat24+sAlex | | |
| 27 | TECE-OAM-RRA-1.0 | 0.4 | 42.4 | 1.3 | 21.6 | 0.81 | 0.82 | 44.5 | 0.599 | 402k | 42.82 | 222M | EFSG | 2026-07-05 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex | | |
| 28 | DPA-4.0.1-Pro-MPtrj | 0.6 | 59.4 | 1.4 | 21.6 | 0.77 | 0.79 | 47.4 | 0.598 | 264k | 28.1 | 22.8M | EFSG | 2026-06-11 | 6 Å | 146k (1.58M) MPtrj | | |
| 29 | eqV2 M | 0.6 | 44.6 | 1.4 | 22.3 | 0.90 | 0.91 | 58.2 | 0.595 | 151k | 16.12 | 86.6M | EFSD | 2024-10-18 | 12 Å | 3.37M (102M) MPtrj+OMat24 | ||
| 30 | eSEN-30M-MP | 0.9 | 81.4 | 1.9 | 22.8 | 0.78 | 0.79 | 51.5 | 0.593 | 214k | 22.82 | 30.1M | EFSG | 2025-03-17 | 6 Å | 146k (1.58M) MPtrj | ||
| 31 | MatRIS-10M-MP | 0.9 | 100.4 | 1.6 | 22.2 | 0.81 | 0.82 | 45.9 | 0.593 | 508k | 54.2 | 10.4M | EFSGM | 2025-10-29 | 6 Å | 146k (1.58M) MPtrj |  | |
| 32 | MatRIS-10M-OAM | 0.7 | 71.9 | 1.5 | 22.0 | 0.83 | 0.83 | 50.0 | 0.581 | 511k | 54.52 | 10.4M | EFSGM | 2025-10-29 | 6 Å | 6.6M (113M) MPtrj+OMat24+sAlex | | |
| 33 | Allegro-MP-L | 1.1 | 114.7 | 1.7 | 24.3 | 0.81 | 0.82 | 56.5 | 0.556 | 289k | 30.77 | 18.7M | EFSG | 2025-09-08 | 6 Å | 146k (1.58M) MPtrj | | |
| 34 | CHGNet | 2.4 | 202.2 | 6.3 | 35.6 | 1.11 | 1.12 | 65.9 | 0.554 | 87k | 9.28 | 413k | EFSGM | 2023-03-03 | 5 Å | 146k (1.58M) MPtrj |   |
CMDS vs Speed
This defaults to a cost-vs-fidelity Pareto: each model's total rollout wall time against its CMDS, with marker size showing model parameters and color the training-set size. Use the axis/color/size selectors to compare any pair of metrics: the RDF, ADF and vDOS errors range from 0% (perfect match with the AIMD reference) to 100% (as different from the reference as an ideal gas / non-overlapping distributions).