Table 1 Mean absolute errors for atomisation energies \(U_0\) in kcal/mol, HOMO and LUMO energies (in eV) for several models Kernel Ridge regression (KRR), Elastic Net (EN), Gaussian process regression (KRR), and neural networks (NN) reported in the literature (from oldest to most recent)
From: Dataset’s chemical diversity limits the generalizability of machine learning predictions
References | ML method/descriptor | Dataset | (Training–Test sizes) | \(U_0\) | HOMO | LUMO |
|---|---|---|---|---|---|---|
Rupp [12] | KRR/CM | QM7 | (7000–165) | 10.0 | – | – |
Montavon [21] | multitask NN | QM7b | (CV 5000–2211) | 3.7 | 0.15 | 0.13 |
Hansen [14] | KRR/BoB | QM7 | (CV 5732–1433) | 1.5 | – | – |
Huang [16] | KRR/BoB | QM7b | (5011–2200) | 1.8 | 0.15 | 0.16 |
Huang [16] | KRR/BAML | QM7b | (5011–2200) | 1.2 | 0.10 | 0.11 |
Faber [17] | EN/CM | QM9 | (CV 118k–13k) | 21.0 | 0.34 | 0.63 |
Faber [17] | EN/BoB | QM9 | (CV 118k–13k) | 13.9 | 0.28 | 0.52 |
Faber [17] | KRR/CM | QM9 | (CV 118k–13k) | 3.0 | 0.13 | 0.18 |
Faber [17] | KRR/BoB | QM9 | (CV 118k–13k) | 1.5 | 0.09 | 0.12 |
Faber [17] | KRR/BAML | QM9 | (CV 118k–13k) | 1.2 | 0.09 | 0.12 |
Bartók [19] | GPR/SOAP-GAP | QM7b | (5411–1800) | 0.40 | – | – |
Bartók [19] | GPR/SOAP-GAP | QM9 | (100k–31k) | 0.28 | – | – |
Gilmer [23] | NMP NN | QM9 | (120k–10k) | 0.45 | 0.04 | 0.04 |
Smith [22] | ANI-1 NN | ANI | (13.7M–1.7M) | <1.5 | – | – |
Hou [26] | multitask NN | QM9 | (119k–13k) | 44.0 | 0.38 | 0.63 |
Schütt [24] | SchNet NN | QM9 | (CV 110k–10k) | 0.32 | 0.04 | 0.03 |
Lubbers [27] | HIP-NN | QM9 | (CV 110k–20k) | 0.26 | – | – |
Unke [28] | HDNN | QM9 | (CV 100k–30k) | 0.41 | – | – |
Willatt [30] | KRR/SOAP | QM9 | (CV 100k–30k) | 0.14 | – | – |
Unke [2] | PhysNet NN | QM9 | (CV 110k–20k) | 0.14 | – | – |