AIQM1 is a neural network potential (NNP) method from Peikun Zheng and co-workers at Xiamen University and CMU in 2021.
AIQM1 is released under the MIT license. Read the AIQM1 paper. See the AIQM1 code.
| About AIQM1 | |
|---|---|
| Architecture | custom |
| Dataset | ANI-1x and ANI-1ccx |
| Dataset Level of Theory | ωB97X/def2-TZVPP // 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 | |
| AIQM1 | 2.84 | 6.34 | 13.29 | 10.42 | 7.06 | 8.79 | 5 | 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 |