OMol25 eSEN is a neural network potential (NNP) method from Daniel Levine and co-workers at Meta's FAIR Chemistry in 2025.
Read the OMol25 eSEN preprint. See the OMol25 eSEN code.
About OMol25 eSEN | |
---|---|
Architecture | eSEN |
Dataset | OMol25 |
Dataset Level of Theory | ωB97M-V/def2-TZVPD |
Dataset Size | 101M |
Use OMol25 eSEN to optimize structures, scan dihedrals, and much more with Rowan's simple GUI and cloud-based computing. We offer 500 free credits when you first sign up, and an additional 20 credits each week. Make a free account and get your first results within minutes!
Start computing →GMTKN55 (download) is a high-accuracy molecular benchmark that measures basic properties, reaction energies, and noncovalent interactions. The GMTKN55 benchmark comprises 55 subsets. Each subset contains a number of relative energy measurements computed at the CCSD(T) level of theory. When viewing subset-level results, the mean absolute deviation/error (MAD/MAE) in kcal/mol is shown. Each subset is assigned a difficulty weight (see Section 4). When viewing category-level results, the weighted total mean absolute deviation (using the WTMAD-2 weights) is shown. See all GMTKN55 results.
Name | Benchmarked By | |||||||
---|---|---|---|---|---|---|---|---|
ωB97M-D3BJ/def2-QZVP | 2.86 | 5.77 | 2.34 | 4.54 | 3.63 | 4.04 | link | |
OMol25's eSEN Conserving Small | 8.67 | 3.41 | 2.89 | 5.23 | 2.79 | 4.40 | ||
B3LYP-D3BJ/def2-QZVP | 7.99 | 10.16 | 4.11 | 5.65 | 4.82 | 6.18 | link | |
B97-3c | 11.99 | 10.51 | 9.17 | 8.62 | 11.89 | 10.16 | link | |
GFN2-xTB | 20.02 | 19.77 | 13.66 | 24.58 | 11.44 | 18.65 | link |
The Folmsbee conformers benchmark (GitHub) is a high-accuracy molecular benchmark that evaluates the accuracy of predicting and ranking conformer relative energies. The benchmark contains 708 subsets of 10 conformers each, 632 of which have energies computed at the DLPNO-CCSD(T) level of theory. DLPNO-CCSD(T) calculations were not finished for 76 subsets, so every level of theory will show at least 76 incomplete subsets. Mean absolute error (MAE) and root mean square error (RMSE) are shown in kcal/mol. See all Folmsbee results.
Name | Benchmarked By | |||||
---|---|---|---|---|---|---|
OMol25's eSEN Conserving Small | 0.21 | 0.33 | 0.85 | 0.86 | 76 | |
ωB97X-D/def2-TZVP | 0.24 | 0.39 | 0.83 | 0.85 | 80 | link |
B97-3c | 0.30 | 0.53 | 0.80 | 0.82 | 77 | link |
GFN2-xTB | 0.72 | 1.32 | 0.57 | 0.59 | 76 | link |
This is a benchmark that evaluates the average speed of running energy and force calculations on 10-, 100-, and 1000-atom molecular and periodic systems. All calculations were run on Nvidia A10G GPUs through Modal. See all speed results.
Name | ||||||
---|---|---|---|---|---|---|
OMol25's eSEN Conserving Small | 0.15 | 0.15 | 0.18 | 0.15 | 0.15 | 0.18 |
TorsionNet206 is a high-accuracy molecular benchmark that evaluates the accuracy of predicting and ranking of dihedral energy profiles. The benchmark contains 206 subsets of 24 conformers each computed at the CCSD(T)/def2-TZVP level of theory. Mean absolute error (MAE) and root mean square error (RMSE) are shown in kcal/mol. See all TorsionNet206 results.
Name | Benchmarked By | |||||
---|---|---|---|---|---|---|
OMol25's eSEN Conserving Small | 0.14 | 0.17 | 0.99 | 0.98 | ||
ωB97M-D3BJ/vDZP | 0.15 | 0.18 | 0.99 | 0.98 | ||
B97-3c | 0.35 | 0.45 | 0.98 | 0.97 | ||
GFN2-xTB | 0.73 | 0.91 | 0.85 | 0.85 |
Wiggle150 is a high-accuracy molecular benchmark that evaluates the accuracy of predicting and ranking of highly-strained conformers. The benchmark contains 150 highly-strained conformations of adenosine, benzylpenicillin, and efavirenz computed at the DLPNO–CCSD(T)/CBS level of theory. Mean absolute error (MAE) and root mean square error (RMSE) are shown in kcal/mol. See all Wiggle150 results.
Name | Benchmarked By | ||
---|---|---|---|
OMol25's eSEN Conserving Small | 0.91 | 1.25 | |
ωB97M-D3BJ/def2-QZVP | 1.18 | 1.59 | link |
B3LYP-D3BJ/def2-QZVP | 1.41 | 1.84 | link |
B97-3c | 2.32 | 2.96 | link |
GFN2-xTB | 14.60 | 15.20 | link |