AIQM2 is a neural network potential (NNP) method from Yuxinxin Chen and Pavlo O. Dral at Xiamen University in 2024.
AIQM2 is released under the MIT license. Read the AIQM2 preprint. See the AIQM2 code.
| About AIQM2 | |
|---|---|
| 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 | |
| AIQM2 | 5.29 | 3.70 | 6.25 | 9.25 | 8.21 | 7.20 | 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 |