MACE-OFF23 is a neural network potential (NNP) method from Dávid Péter Kovács, J. Harry Moore, and co-workers in Gábor Csányi's lab at Cambridge in 2023.
MACE-OFF23 is released under the ASL license. Read the MACE-OFF23 preprint. See the MACE-OFF23 code.
| About MACE-OFF23 | |
|---|---|
| Architecture | MACE |
| Dataset | SPICE and additions |
| Dataset Level of Theory | ωB97M-D3BJ/def2-TZVPPD |
| Dataset Size | |
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 | Incomplete Subsets | Benchmarked By | ||||||
|---|---|---|---|---|---|---|---|---|
| ωB97M-D3BJ/def2-QZVP | 2.86 | 5.77 | 2.34 | 4.54 | 3.63 | 4.04 | link | |
| 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 | |
| MACE-OFF23(L) | 8.74 | 10.40 | 22.52 | 6.79 | 37.84 | 16.46 | 6 | I.B. 2024-11-28 |
| MACE-OFF23(M) | 9.08 | 10.50 | 21.49 | 9.12 | 41.58 | 18.23 | 6 | I.B. 2024-11-28 |
| MACE-OFF23(S) | 8.98 | 8.85 | 13.55 | 12.14 | 41.96 | 18.48 | 6 | I.B. 2024-11-28 |
| GFN2-xTB | 20.02 | 19.77 | 13.66 | 24.58 | 11.44 | 18.65 | link |