results

Summary of Benchmark Training

Note that these are the results for models within kgcnn implementation, and that training is not always done with optimal hyperparameter or splits, when comparing with literature. This table is generated automatically from keras history logs. Model weights and training statistics plots are not uploaded on github due to their file size.

Max. or Min. denotes the best test error observed for any epoch during training. To show overall best test error run python3 summary.py --min_max True. If not noted otherwise, we use a (fixed) random k-fold split for validation errors.

ClinToxDataset

ClinTox (MoleculeNet) consists of 1478 compounds as smiles and data of drugs approved by the FDA and those that have failed clinical trials for toxicity reasons. We use random 5-fold cross-validation. The first label 'approved' is chosen as target.

model	kgcnn	epochs	Accuracy	AUC(ROC)
DMPNN	4.0.0	50	0.9480 ± 0.0138	0.8297 ± 0.0568
GAT	4.0.0	50	0.9480 ± 0.0070	0.8512 ± 0.0468
GATv2	4.0.0	50	0.9372 ± 0.0155	0.8587 ± 0.0754
GCN	4.0.0	50	0.9432 ± 0.0155	0.8555 ± 0.0593
GIN	4.0.0	50	0.9412 ± 0.0034	0.8066 ± 0.0636
GraphSAGE	4.0.0	100	0.9412 ± 0.0073	0.8013 ± 0.0422
Schnet	4.0.0	50	0.9277 ± 0.0102	0.6562 ± 0.0760

CoraDataset

Cora Dataset of 19793 publications and 8710 sparse node attributes and 70 node classes. Here we use random 5-fold cross-validation on nodes.

model	kgcnn	epochs	Categorical accuracy
DMPNN	4.0.0	300	0.2476 ± 0.1706
GAT	4.0.0	250	0.6157 ± 0.0071
GATv2	4.0.0	1000	0.6211 ± 0.0048
GCN	4.0.0	300	0.6232 ± 0.0054
GIN	4.0.0	800	0.6263 ± 0.0080
GraphSAGE	4.0.0	600	0.6151 ± 0.0053

CoraLuDataset

Cora Dataset after Lu et al. (2003) of 2708 publications and 1433 sparse attributes and 7 node classes. Here we use random 5-fold cross-validation on nodes.

model	kgcnn	epochs	Categorical accuracy
DMPNN	4.0.0	300	0.8357 ± 0.0156
GAT	4.0.0	250	0.8397 ± 0.0122
GATv2	4.0.0	250	0.8331 ± 0.0104
GCN	4.0.0	300	0.8072 ± 0.0109
GIN	4.0.0	500	0.8279 ± 0.0170
GraphSAGE	4.0.0	500	0.8497 ± 0.0100

ESOLDataset

ESOL consists of 1128 compounds as smiles and their corresponding water solubility in log10(mol/L). We use random 5-fold cross-validation.

model	kgcnn	epochs	MAE [log mol/L]	RMSE [log mol/L]
AttentiveFP	4.0.0	200	0.4351 ± 0.0110	0.6080 ± 0.0207
CMPNN	4.0.0	600	0.5276 ± 0.0154	0.7505 ± 0.0189
DGIN	4.0.0	300	0.4434 ± 0.0252	0.6225 ± 0.0420
DMPNN	4.0.0	300	0.4401 ± 0.0165	0.6203 ± 0.0292
EGNN	4.0.0	800	0.4507 ± 0.0152	0.6563 ± 0.0370
GAT	4.0.0	500	0.4818 ± 0.0240	0.6919 ± 0.0694
GATv2	4.0.0	500	0.4598 ± 0.0234	0.6650 ± 0.0409
GCN	4.0.0	800	0.4613 ± 0.0205	0.6534 ± 0.0513
GIN	4.0.0	300	0.5369 ± 0.0334	0.7954 ± 0.0861
GNNFilm	4.0.0	800	0.4854 ± 0.0368	0.6724 ± 0.0436
GraphSAGE	4.0.0	500	0.4874 ± 0.0228	0.6982 ± 0.0608
HamNet	4.0.0	400	0.5479 ± 0.0143	0.7417 ± 0.0298
HDNNP2nd	4.0.0	500	0.7857 ± 0.0986	1.0467 ± 0.1367
INorp	4.0.0	500	0.5055 ± 0.0436	0.7297 ± 0.0786
MAT	4.0.0	400	0.5064 ± 0.0299	0.7194 ± 0.0630
MEGAN	4.0.0	400	0.4281 ± 0.0201	0.6062 ± 0.0252
Megnet	4.0.0	800	0.5679 ± 0.0310	0.8196 ± 0.0480
MoGAT	4.0.0	200	0.4797 ± 0.0114	0.6533 ± 0.0114
MXMNet	4.0.0	900	0.6486 ± 0.0633	1.0123 ± 0.2059
NMPN	4.0.0	800	0.5046 ± 0.0266	0.7193 ± 0.0607
PAiNN	4.0.0	250	0.4857 ± 0.0598	0.6650 ± 0.0674
RGCN	4.0.0	800	0.4703 ± 0.0251	0.6529 ± 0.0318
rGIN	4.0.0	300	0.5196 ± 0.0351	0.7142 ± 0.0263
Schnet	4.0.0	800	0.4777 ± 0.0294	0.6977 ± 0.0538

FreeSolvDataset

FreeSolv (MoleculeNet) consists of 642 compounds as smiles and their corresponding hydration free energy for small neutral molecules in water. We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [log mol/L]	RMSE [log mol/L]
CMPNN	4.0.0	600	0.5202 ± 0.0504	0.9339 ± 0.1286
DGIN	4.0.0	300	0.5489 ± 0.0374	0.9448 ± 0.0787
DimeNetPP	4.0.0	872	0.6167 ± 0.0719	1.0302 ± 0.1717
DMPNN	4.0.0	300	0.5487 ± 0.0754	0.9206 ± 0.1889
EGNN	4.0.0	800	0.5386 ± 0.0548	1.0363 ± 0.1237
GAT	4.0.0	500	0.6051 ± 0.0861	1.0326 ± 0.1819
GATv2	4.0.0	500	0.6151 ± 0.0247	1.0535 ± 0.0817
GCN	4.0.0	800	0.6400 ± 0.0834	1.0876 ± 0.1393
GIN	4.0.0	300	0.8100 ± 0.1016	1.2695 ± 0.1192
GNNFilm	4.0.0	800	0.6562 ± 0.0552	1.1597 ± 0.1245
GraphSAGE	4.0.0	500	0.5894 ± 0.0675	1.0009 ± 0.1491
HamNet	4.0.0	400	0.6619 ± 0.0428	1.1410 ± 0.1120
HDNNP2nd	4.0.0	500	1.0201 ± 0.1559	1.6351 ± 0.3419
INorp	4.0.0	500	0.6612 ± 0.0188	1.1155 ± 0.1061
MAT	4.0.0	400	0.8115 ± 0.0649	1.3099 ± 0.1235
MEGAN	4.0.0	400	0.6303 ± 0.0550	1.0429 ± 0.1031
Megnet	4.0.0	800	0.8878 ± 0.0528	1.4134 ± 0.1200
MoGAT	4.0.0	200	0.7097 ± 0.0374	1.0911 ± 0.1334
MXMNet	4.0.0	900	1.1386 ± 0.1979	3.0487 ± 2.1757
RGCN	4.0.0	800	0.5128 ± 0.0810	0.9228 ± 0.1887
rGIN	4.0.0	300	0.8503 ± 0.0613	1.3285 ± 0.0976
Schnet	4.0.0	800	0.6070 ± 0.0285	1.0603 ± 0.0549

ISO17Dataset

The database consist of 129 molecules each containing 5,000 conformational geometries, energies and forces with a resolution of 1 femtosecond in the molecular dynamics trajectories. The molecules were randomly drawn from the largest set of isomers in the QM9 dataset.

model	kgcnn	epochs	Energy (test_within)	Force (test_within)
Schnet.EnergyForceModel	4.0.0	1000	0.0061 ± nan	0.0134 ± nan

LipopDataset

Lipophilicity (MoleculeNet) consists of 4200 compounds as smiles. Graph labels for regression are octanol/water distribution coefficient (logD at pH 7.4). We use random 5-fold cross-validation.

model	kgcnn	epochs	MAE [log mol/L]	RMSE [log mol/L]
DMPNN	4.0.0	300	0.3814 ± 0.0064	0.5462 ± 0.0095
GAT	4.0.0	500	0.5168 ± 0.0088	0.7220 ± 0.0098
GATv2	4.0.0	500	0.4342 ± 0.0104	0.6056 ± 0.0114
GCN	4.0.0	800	0.4960 ± 0.0107	0.6833 ± 0.0155
GIN	4.0.0	300	0.4745 ± 0.0101	0.6658 ± 0.0159
GraphSAGE	4.0.0	500	0.4333 ± 0.0217	0.6218 ± 0.0318
Schnet	4.0.0	800	0.5657 ± 0.0202	0.7485 ± 0.0245

MD17Dataset

Energies and forces for molecular dynamics trajectories of eight organic molecules. All geometries in A, energy labels in kcal/mol and force labels in kcal/mol/A. We use preset train-test split. Training on 1000 geometries, test on 500/1000 geometries. Errors are MAE for forces. Results are for the CCSD and CCSD(T) data in MD17.

model	kgcnn	epochs	Aspirin	Toluene	Malonaldehyde	Benzene	Ethanol
PAiNN.EnergyForceModel	4.0.0	1000	0.8551 ± nan	0.2815 ± nan	0.7749 ± nan	0.0427 ± nan	0.5805 ± nan
Schnet.EnergyForceModel	4.0.0	1000	1.2173 ± nan	0.7395 ± nan	0.8444 ± nan	0.3353 ± nan	0.4832 ± nan

MD17RevisedDataset

Energies and forces for molecular dynamics trajectories. All geometries in A, energy labels in kcal/mol and force labels in kcal/mol/A. We use preset train-test split. Training on 1000 geometries, test on 500/1000 geometries. Errors are MAE for forces.

model	kgcnn	epochs	Aspirin	Toluene	Malonaldehyde	Benzene	Ethanol
Schnet.EnergyForceModel	4.0.0	1000	1.0389 ± 0.0071	0.5482 ± 0.0105	0.6727 ± 0.0132	0.2525 ± 0.0091	0.4471 ± 0.0199

MatProjectDielectricDataset

Materials Project dataset from Matbench with 4764 crystal structures and their corresponding Refractive index (unitless). We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [no unit]	RMSE [no unit]
CGCNN.make_crystal_model	4.0.0	1000	0.3306 ± 0.0602	1.9736 ± 0.7324
DimeNetPP.make_crystal_model	4.0.0	780	0.3415 ± 0.0542	1.9637 ± 0.6323
Megnet.make_crystal_model	4.0.0	1000	0.3362 ± 0.0550	2.0156 ± 0.5872
NMPN.make_crystal_model	4.0.0	700	0.3289 ± 0.0489	1.8770 ± 0.6522
PAiNN.make_crystal_model	4.0.0	800	0.3539 ± 0.0433	1.8661 ± 0.5984
Schnet.make_crystal_model	4.0.0	800	0.3180 ± 0.0359	1.8509 ± 0.5854

MatProjectEFormDataset

Materials Project dataset from Matbench with 132752 crystal structures and their corresponding formation energy in [eV/atom]. We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [eV/atom]	RMSE [eV/atom]
CGCNN.make_crystal_model	4.0.0	1000	0.0298 ± 0.0002	0.0747 ± 0.0029
Megnet.make_crystal_model	4.0.1	1000	0.0272 ± nan	0.0700 ± nan
PAiNN.make_crystal_model	4.0.1	800	0.0241 ± 0.0003	0.0558 ± 0.0018
Schnet.make_crystal_model	4.0.0	800	0.0211 ± 0.0003	0.0510 ± 0.0024

MatProjectGapDataset

Materials Project dataset from Matbench with 106113 crystal structures and their band gap as calculated by PBE DFT from the Materials Project, in eV. We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [eV]	RMSE [eV]
CGCNN.make_crystal_model	4.0.0	1000	0.2039 ± 0.0050	0.4882 ± 0.0213
Megnet.make_crystal_model	4.0.1	1000	0.2028 ± 0.0041	0.4871 ± 0.0109
PAiNN.make_crystal_model	4.0.1	800	0.2250 ± 0.0128	1.4224 ± 1.7103
Schnet.make_crystal_model	4.0.0	800	1.2226 ± 1.0573	58.3713 ± 114.2957

MatProjectIsMetalDataset

Materials Project dataset from Matbench with 106113 crystal structures and their corresponding Metallicity determined with pymatgen. 1 if the compound is a metal, 0 if the compound is not a metal. We use a random 5-fold cross-validation.

model	kgcnn	epochs	Accuracy	AUC
CGCNN.make_crystal_model	4.0.0	100	0.8910 ± 0.0027	0.9406 ± 0.0024
Megnet.make_crystal_model	4.0.0	100	0.8966 ± 0.0033	0.9506 ± 0.0026
PAiNN.make_crystal_model	4.0.0	80	0.8989 ± 0.0034	0.9338 ± 0.0035
Schnet.make_crystal_model	4.0.0	80	0.8953 ± 0.0058	0.9506 ± 0.0053

MatProjectJdft2dDataset

Materials Project dataset from Matbench with 636 crystal structures and their corresponding Exfoliation energy (meV/atom). We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [meV/atom]	RMSE [meV/atom]
CGCNN.make_crystal_model	4.0.0	1000	57.6974 ± 18.0803	140.6167 ± 44.8418
DimeNetPP.make_crystal_model	4.0.0	780	50.2880 ± 11.4199	126.0600 ± 38.3769
Megnet.make_crystal_model	4.0.0	1000	51.1735 ± 9.1746	123.4178 ± 32.9582
NMPN.make_crystal_model	4.0.0	700	59.3986 ± 10.9272	139.5943 ± 32.1129
PAiNN.make_crystal_model	4.0.0	800	49.3889 ± 11.5376	121.7087 ± 30.0472
Schnet.make_crystal_model	4.0.0	800	45.2412 ± 11.6395	115.6890 ± 39.0929

MatProjectLogGVRHDataset

Materials Project dataset from Matbench with 10987 crystal structures and their corresponding Base 10 logarithm of the DFT Voigt-Reuss-Hill average shear moduli in GPa. We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [log(GPa)]	RMSE [log(GPa)]
CGCNN.make_crystal_model	4.0.0	1000	0.0874 ± 0.0022	0.1354 ± 0.0056
DimeNetPP.make_crystal_model	4.0.0	780	0.0839 ± 0.0027	0.1290 ± 0.0065
Megnet.make_crystal_model	4.0.0	1000	0.0885 ± 0.0017	0.1360 ± 0.0054
NMPN.make_crystal_model	4.0.0	700	0.0874 ± 0.0027	0.1324 ± 0.0045
PAiNN.make_crystal_model	4.0.0	800	0.0870 ± 0.0033	0.1332 ± 0.0103
Schnet.make_crystal_model	4.0.0	800	0.0836 ± 0.0021	0.1296 ± 0.0044

MatProjectLogKVRHDataset

Materials Project dataset from Matbench with 10987 crystal structures and their corresponding Base 10 logarithm of the DFT Voigt-Reuss-Hill average bulk moduli in GPa. We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [log(GPa)]	RMSE [log(GPa)]
CGCNN.make_crystal_model	4.0.0	1000	0.0672 ± 0.0012	0.1265 ± 0.0042
DimeNetPP.make_crystal_model	4.0.0	780	0.0604 ± 0.0023	0.1141 ± 0.0055
Megnet.make_crystal_model	4.0.0	1000	0.0686 ± 0.0016	0.1285 ± 0.0061
NMPN.make_crystal_model	4.0.0	700	0.0688 ± 0.0009	0.1262 ± 0.0031
PAiNN.make_crystal_model	4.0.0	800	0.0649 ± 0.0007	0.1170 ± 0.0048
Schnet.make_crystal_model	4.0.0	800	0.0635 ± 0.0016	0.1186 ± 0.0044

MatProjectPerovskitesDataset

Materials Project dataset from Matbench with 18928 crystal structures and their corresponding Heat of formation of the entire 5-atom perovskite cell in eV. We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [eV]	RMSE [eV]
CGCNN.make_crystal_model	4.0.0	1000	0.0425 ± 0.0011	0.0712 ± 0.0037
DimeNetPP.make_crystal_model	4.0.0	780	0.0447 ± 0.0016	0.0730 ± 0.0050
Megnet.make_crystal_model	4.0.0	1000	0.0388 ± 0.0017	0.0675 ± 0.0041
NMPN.make_crystal_model	4.0.0	700	0.0381 ± 0.0009	0.0652 ± 0.0029
PAiNN.make_crystal_model	4.0.0	800	0.0474 ± 0.0003	0.0762 ± 0.0017
Schnet.make_crystal_model	4.0.0	800	0.0381 ± 0.0005	0.0645 ± 0.0024

MatProjectPhononsDataset

Materials Project dataset from Matbench with 1,265 crystal structures and their corresponding vibration properties in [1/cm]. We use a random 5-fold cross-validation.

model	kgcnn	epochs	MAE [eV/atom]	RMSE [eV/atom]
CGCNN.make_crystal_model	4.0.0	1000	42.6447 ± 4.5721	92.1627 ± 21.4345
DimeNetPP.make_crystal_model	4.0.0	780	39.8893 ± 3.1280	77.5776 ± 16.0908
Megnet.make_crystal_model	4.0.0	1000	30.6620 ± 2.9013	60.8733 ± 17.1448
NMPN.make_crystal_model	4.0.0	700	45.9344 ± 5.7908	95.4136 ± 35.5401
PAiNN.make_crystal_model	4.0.0	800	47.5408 ± 4.2815	86.6761 ± 11.9220
Schnet.make_crystal_model	4.0.0	800	43.0692 ± 3.6227	88.5151 ± 20.0244

MUTAGDataset

MUTAG dataset from TUDataset for classification with 188 graphs. We use random 5-fold cross-validation.

model	kgcnn	epochs	Accuracy	AUC(ROC)
DMPNN	4.0.0	300	0.8407 ± 0.0463	0.8567 ± 0.0511
GAT	4.0.0	500	0.8141 ± 0.1077	0.8671 ± 0.0923
GATv2	4.0.0	500	0.8193 ± 0.0945	0.8379 ± 0.1074
GCN	4.0.0	800	0.7716 ± 0.0531	0.7956 ± 0.0909
GIN	4.0.0	300	0.8091 ± 0.0781	0.8693 ± 0.0855
GraphSAGE	4.0.0	500	0.8357 ± 0.0798	0.8533 ± 0.0824

MutagenicityDataset

Mutagenicity dataset from TUDataset for classification with 4337 graphs. The dataset was cleaned for unconnected atoms. We use random 5-fold cross-validation.

model	kgcnn	epochs	Accuracy	AUC(ROC)
DMPNN	4.0.0	300	0.8266 ± 0.0059	0.8708 ± 0.0076
GAT	4.0.0	500	0.7989 ± 0.0114	0.8290 ± 0.0112
GATv2	4.0.0	200	0.7674 ± 0.0048	0.8423 ± 0.0064
GCN	4.0.0	800	0.7955 ± 0.0154	0.8191 ± 0.0137
GIN	4.0.0	300	0.8118 ± 0.0091	0.8492 ± 0.0077
GraphSAGE	4.0.0	500	0.8195 ± 0.0126	0.8515 ± 0.0083

PROTEINSDataset

TUDataset of proteins that are classified as enzymes or non-enzymes. Nodes represent the amino acids of the protein. We use random 5-fold cross-validation.

model	kgcnn	epochs	Accuracy	AUC(ROC)
DMPNN	4.0.0	300	0.7287 ± 0.0253	0.7970 ± 0.0343
GAT	4.0.0	500	0.7314 ± 0.0357	0.7899 ± 0.0468
GATv2	4.0.0	500	0.6720 ± 0.0595	0.6850 ± 0.0938
GCN	4.0.0	800	0.7017 ± 0.0303	0.7211 ± 0.0254
GIN	4.0.0	150	0.7224 ± 0.0343	0.7905 ± 0.0528
GraphSAGE	4.0.0	500	0.7009 ± 0.0398	0.7263 ± 0.0453

QM7Dataset

QM7 dataset is a subset of GDB-13. Molecules of up to 23 atoms (including 7 heavy atoms C, N, O, and S), totalling 7165 molecules. We use dataset-specific 5-fold cross-validation. The atomization energies are given in kcal/mol and are ranging from -800 to -2000 kcal/mol).

model	kgcnn	epochs	MAE [kcal/mol]	RMSE [kcal/mol]
DimeNetPP	4.0.0	872	3.4639 ± 0.2003	7.5327 ± 1.8190
EGNN	4.0.0	800	1.7300 ± 0.1336	5.1268 ± 2.5134
Megnet	4.0.0	800	1.5180 ± 0.0802	3.0321 ± 0.1936
MXMNet	4.0.0	900	1.2431 ± 0.0820	2.6694 ± 0.2584
NMPN	4.0.0	500	7.2907 ± 0.9061	38.1446 ± 12.1445
PAiNN	4.0.0	872	1.5765 ± 0.0742	5.2705 ± 2.2848
Schnet	4.0.0	800	3.4313 ± 0.4757	10.8978 ± 7.3863

QM9Dataset

QM9 dataset of 134k stable small organic molecules made up of C,H,O,N,F. Labels include geometric, energetic, electronic, and thermodynamic properties. We use a random 5-fold cross-validation, but not all splits are evaluated for cheaper evaluation. Test errors are MAE and for energies are given in [eV].

model	kgcnn	epochs	HOMO [eV]	LUMO [eV]	U0 [eV]	H [eV]	G [eV]
DimeNetPP	4.0.1	600	0.0291 ± 0.0004	0.0284 ± 0.0005	0.0097 ± 0.0002	0.0100 ± 0.0004	0.0108 ± 0.0005
EGNN	4.0.1	800	0.0307 ± 0.0008	0.0265 ± 0.0005	0.0096 ± 0.0002	0.0098 ± 0.0003	0.0108 ± 0.0003
Megnet	4.0.1	800	0.0478 ± 0.0012	0.0407 ± 0.0009	0.0169 ± 0.0005	0.0168 ± 0.0005	0.0169 ± 0.0006
MXMNet	4.0.1	900	0.0342 ± 0.0016	0.0292 ± 0.0022	0.0105 ± 0.0051	0.0095 ± 0.0034	0.0102 ± 0.0013
NMPN	4.0.1	700	0.0844 ± 0.0004	0.0885 ± 0.0019	0.0541 ± 0.0014	0.0546 ± 0.0011	0.0528 ± 0.0010
PAiNN	4.0.0	872	0.0483 ± 0.0275	0.0268 ± 0.0002	0.0099 ± 0.0003	0.0101 ± 0.0003	0.0110 ± 0.0002
Schnet	4.0.0	800	0.0402 ± 0.0007	0.0340 ± 0.0001	0.0142 ± 0.0002	0.0146 ± 0.0002	0.0143 ± 0.0002

SIDERDataset

SIDER (MoleculeNet) consists of 1427 compounds as smiles and data for adverse drug reactions (ADR), grouped into 27 system organ classes. We use random 5-fold cross-validation.

model	kgcnn	epochs	Accuracy	AUC(ROC)
DMPNN	4.0.0	50	0.7519 ± 0.0055	0.6280 ± 0.0173
GAT	4.0.0	50	0.7595 ± 0.0034	0.6224 ± 0.0106
GATv2	4.0.0	50	0.7548 ± 0.0052	0.6152 ± 0.0154
GIN	4.0.0	50	0.7472 ± 0.0055	0.5995 ± 0.0058
GraphSAGE	4.0.0	30	0.7547 ± 0.0043	0.6038 ± 0.0108
Schnet	4.0.0	50	0.7583 ± 0.0076	0.6119 ± 0.0159

Tox21MolNetDataset

Tox21 (MoleculeNet) consists of 7831 compounds as smiles and 12 different targets relevant to drug toxicity. We use random 5-fold cross-validation.

model	kgcnn	epochs	Accuracy	AUC(ROC)	BACC
DMPNN	4.0.0	50	0.9272 ± 0.0024	0.8321 ± 0.0103	0.6995 ± 0.0130
EGNN	4.0.1	50	0.9164 ± 0.0030	0.7655 ± 0.0080	0.6839 ± 0.0053
GAT	4.0.0	50	0.9243 ± 0.0022	0.8279 ± 0.0092	0.6504 ± 0.0074
GATv2	4.0.0	50	0.9222 ± 0.0019	0.8251 ± 0.0069	0.6760 ± 0.0140
GIN	4.0.0	50	0.9220 ± 0.0024	0.7986 ± 0.0180	0.6741 ± 0.0143
GraphSAGE	4.0.0	100	0.9180 ± 0.0027	0.7976 ± 0.0087	0.6755 ± 0.0047
Schnet	4.0.0	50	0.9128 ± 0.0030	0.7719 ± 0.0139	0.6639 ± 0.0162