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Compare machine-learning models across atomistic modeling tasks.

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Discovery test set:

43 of 53 eligible models · Sorted by CPS (descending) · Discovery: Unique Prototypes · Row numbers follow this filtered order.

CPS combines discovery F1 (50%), geometry RMSD (10%), and phonons κSRME (40%). MD and diatomics are excluded. CPS uses unique-prototype discovery scores.

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1 Prophet-OAME-MBD 0.912 0.978 0.928 6.065 0.927 0.018 0.869 0.065 0.059 0.578 0.572 62.3M EFSG 2026-09-13 7 Å 16.4M (324M) MPtrj+OMat24+sAlex+ELEMENTA
2 TECE-OAM-RRA-1.0 0.908 0.978 0.929 6.073 0.928 0.018 0.871 0.093 0.058 0.630 0.270 222M EFSG 2026-07-05 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
3 EquFlashV2* 0.907 0.978 0.929 6.069 0.928 0.018 0.873 0.094 0.058 0.651 0.644 44.9M EFSG 2026-06-11 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
4 EquiformerV3+DeNS-OAM 0.902 0.978 0.931 6.074 0.928 0.018 0.868 0.118 0.059 0.638 0.269 30.3M EFSG 2026-04-07 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
5 GRACE-3L-OAM-L 0.900 0.977 0.925 6.041 0.923 0.018 0.875 0.121 0.058 0.739 0.699 42.1M EFSG 2026-07-02 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
6 PET-OAM-XL 0.898 0.977 0.924 6.075 0.929 0.019 0.864 0.119 0.060 0.634 0.388 730M EFSG 2026-01-10 n/a 6.6M (113M) MPtrj+OMat24+sAlex
7 TACE-OAM-L 0.889 0.972 0.910 5.898 0.902 0.020 0.868 0.126 0.061 0.728 0.761 82.9M EFSG 2026-04-09 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
8 eSEN-30M-OAM 0.888 0.977 0.925 6.069 0.928 0.018 0.866 0.17 0.061 0.625 0.648 30.2M EFSG 2025-03-17 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
9 EquFlash* 0.888 0.975 0.919 5.983 0.915 0.019 0.871 0.158 0.060 0.678 0.328 28.7M EFSG 2025-06-23 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
10 Nequip-OAM-XL 0.886 0.971 0.906 5.869 0.897 0.020 0.872 0.125 0.063 0.623 0.796 32.1M EFSG 2025-11-30 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
11 MatRIS-10M-OAM 0.877 0.976 0.921 6.039 0.923 0.019 0.871 0.218 0.060 0.581 0.678 10.4M EFSGM 2025-10-29 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
12 SevenNet-Omni-i12* 0.873 0.971 0.906 5.954 0.910 0.021 0.868 0.192 0.062 0.626 0.696 54.9M EFSG 2026-01-12 6 Å n/a COSMOSDataset
13 Nequip-OAM-L 0.870 0.967 0.893 5.823 0.890 0.022 0.865 0.166 0.065 0.670 0.818 9.6M EFSG 2025-09-08 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
14 GRACE-2L-OAM-L 0.865 0.964 0.883 5.840 0.893 0.022 0.862 0.169 0.064 0.759 0.694 26.4M EFSG 2025-09-09 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
15 ORB v3 0.860 0.971 0.905 5.912 0.904 0.024 0.821 0.21 0.075 0.736 0.834 25.5M EFSG 2025-04-05 6 Å 6.47M (133M) MPtrj+Alex+OMat24
16 DPA-4.0.1-Pro-MPtrj 0.840 0.956 0.857 5.609 0.857 0.029 0.836 0.211 0.069 0.598 0.123 22.8M EFSG 2026-06-11 6 Å 146k (1.58M) MPtrj
17 Allegro-OAM-L 0.840 0.966 0.895 5.674 0.867 0.022 0.868 0.319 0.065 0.602 0.828 9.7M EFSG 2025-09-08 7 Å 6.6M (113M) MPtrj+OMat24+sAlex
18 GRACE-2L-OAM 0.837 0.963 0.880 5.774 0.883 0.023 0.862 0.294 0.067 0.751 0.527 12.6M EFSG 2025-02-06 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
19 EquiformerV3+DeNS-MP 0.830 0.956 0.863 5.479 0.838 0.029 0.840 0.275 0.070 0.622 0.413 30.3M EFSG 2026-04-07 6 Å 146k (1.58M) MPtrj
20 DPA-3.1-3M-FT 0.802 0.963 0.884 5.667 0.866 0.023 0.869 0.469 0.069 0.647 0.533 3.27M EFSG 2025-06-05 6 Å n/a OpenLAM
21 eSEN-30M-MP 0.797 0.946 0.831 5.260 0.804 0.033 0.822 0.34 0.075 0.593 0.417 30.1M EFSG 2025-03-17 6 Å 146k (1.58M) MPtrj
22 MACE-MPA-0 0.795 0.954 0.852 5.582 0.853 0.028 0.842 0.412 0.073 0.659 0.728 9.06M EFSG 2024-12-09 6 Å 3.37M (12M) MPtrj+sAlex
23 MatRIS-10M-MP 0.778 0.951 0.847 5.422 0.829 0.031 0.824 0.489 0.072 0.593 0.264 10.4M EFSGM 2025-10-29 6 Å 146k (1.58M) MPtrj
24 AlphaNet-v1-OAM* 0.769 0.968 0.901 5.747 0.879 0.024 0.831 0.643 0.079 0.653 0.739 4.65M EFSG 2025-05-12 5 Å 6.6M (113M) MPtrj+OMat24+sAlex
25 MatterSim v1 5M 0.767 0.959 0.862 5.852 0.895 0.024 0.863 0.575 0.073 0.659 0.717 4.55M EFSG 2024-12-16 5 Å n/a MatterSim
26 GRACE-1L-OAM 0.761 0.944 0.824 5.255 0.803 0.031 0.842 0.517 0.072 0.749 0.552 3.45M EFSG 2025-02-06 6 Å 6.6M (113M) MPtrj+OMat24+sAlex
27 Eqnorm MPtrj 0.756 0.929 0.786 4.844 0.741 0.040 0.799 0.408 0.084 0.639 0.711 1.31M EFSG 2025-05-26 6 Å 146k (1.58M) MPtrj
28 Nequix MP PFT 0.755 0.914 0.748 4.479 0.685 0.044 0.784 0.307 0.087 0.616 0.565 708k EFSHG 2026-01-08 6 Å 154k (1.59M) MPtrj+MDR-MP PBE ωq
29 Nequip-MP-L 0.730 0.921 0.761 4.704 0.719 0.043 0.791 0.466 0.086 0.629 0.573 9.6M EFSG 2025-09-08 6 Å 146k (1.58M) MPtrj
30 Nequix MP 0.729 0.914 0.751 4.455 0.681 0.044 0.782 0.446 0.085 0.603 0.561 708k EFSG 2025-08-17 6 Å 146k (1.58M) MPtrj
31 Allegro-MP-L 0.720 0.915 0.751 4.516 0.690 0.044 0.778 0.504 0.082 0.556 0.747 18.7M EFSG 2025-09-08 6 Å 146k (1.58M) MPtrj
32 SevenNet-l3i5* 0.714 0.920 0.760 4.629 0.708 0.044 0.776 0.55 0.085 0.651 0.533 1.17M EFSG 2024-12-10 5 Å 146k (1.58M) MPtrj
33 HIENet 0.707 0.929 0.777 4.932 0.754 0.041 0.793 0.642 0.080 0.653 0.649 7.51M EFSG 2025-07-01 5 Å 146k (1.58M) MPtrj
34 GRACE-2L-MPtrj 0.681 0.895 0.691 4.163 0.636 0.052 0.741 0.525 0.090 0.698 0.355 15.3M EFSG 2024-11-21 6 Å 146k (1.58M) MPtrj
35 MACE-MP-0 0.637 0.878 0.669 3.777 0.577 0.057 0.697 0.682 0.091 0.633 0.627 4.69M EFSG 2023-07-14 6 Å 146k (1.58M) MPtrj
36 BAM-MP-core 0.584 0.869 0.623 3.682 0.563 0.060 0.712 0.848 0.086 0.607 0.685 17M EFSG 2026-07-16 6 Å 146k (1.58M) MPtrj
37 eqV2 M 0.558 0.975 0.917 6.047 0.924 0.020 0.848 1.771 0.069 0.595 0.283 86.6M EFSD 2024-10-18 12 Å 3.37M (102M) MPtrj+OMat24
38 ORB v2 MPA 0.528 0.965 0.880 6.041 0.924 0.028 0.824 1.734 0.097 0.750 0.678 25.2M EFSD 2024-10-11 10 Å 3.25M (32.1M) MPtrj+Alex
39 eqV2 S DeNS 0.522 0.939 0.815 5.042 0.771 0.036 0.788 1.676 0.076 0.615 0.258 31.2M EFSD 2024-10-18 12 Å 146k (1.58M) MPtrj
40 ORB v2 MPtrj 0.470 0.922 0.765 4.702 0.719 0.045 0.756 1.726 0.101 0.727 0.667 25.2M EFSD 2024-10-14 10 Å 146k (1.58M) MPtrj
41 M3GNet 0.428 0.812 0.569 2.882 0.441 0.075 0.585 1.409 0.112 0.593 0.747 228k EFSG 2022-09-20 5 Å 62.8k (188k) MPF
42 CHGNet 0.400 0.851 0.613 3.361 0.514 0.063 0.689 1.717 0.095 0.554 0.297 413k EFSGM 2023-03-03 5 Å 146k (1.58M) MPtrj
43 NequIP-GNoME n/a 0.948 0.829 5.523 0.844 0.035 0.785 n/a n/a n/a n/a 16.2M EFG 2024-02-03 5 Å 6M (89M) GNoME
CPS weights
Other weights adjust proportionally. Total: 100%.

CPS Progress Over Time

Each dot is a model; the dashed line tracks the best score so far.

  • Params Params · 43 models
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About Matbench Discovery

This benchmark compares accuracy, robustness, and computational cost across crystal discovery, geometry optimization, phonons, molecular dynamics, and diatomics. Rankings help you explore trade-offs; they are not a complete assessment or an endorsement of a model.

Crystal stability is evaluated against a convex hull built from DFT reference energies.

Riebesell, J., Goodall, R.E.A., Benner, P. et al. A framework to evaluate machine learning crystal stability predictions. Nature Machine Intelligence 7, 836–847 (2025).