ANI-1ccx is a neural network potential (NNP) method from Justin S. Smith, Benjamin T. Nebgen, and co-workers at CMU, the University of Florida, and Los Alamos National Laboratory in 2019.
ANI-1ccx is released under the MIT license. Read the ANI-1ccx preprint. Read the ANI-1ccx paper. See the ANI-1ccx code.
| About ANI-1ccx | |
|---|---|
| Architecture | custom |
| Dataset | custom |
| Dataset Level of Theory | ωB97X/6-31G* // CCSD(T)*/CBS |
| Dataset Size | 5M // 500k |
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 | |
| GFN2-xTB | 20.02 | 19.77 | 13.66 | 24.58 | 11.44 | 18.65 | link | |
| ANI-1ccx | 14.14 | 24.71 | 28.20 | 22.42 | 13 | link |