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5 months ago

CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning

View Paper Details

Ulrik Friis-Jensen Frederik L. Johansen Andy S. Anker Erik B. Dam Kirsten M. Ø. Jensen Raghavendra Selvan

CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning

Abstract

Code Repositories

UlrikFriisJensen/CHILI
Official
pytorch
Mentioned in GitHub

Benchmarks

BenchmarkMethodologyMetrics
atomic-number-classification-on-chili-100kGAT
F1-score (Weighted): 0.192 +/- 0.000
atomic-number-classification-on-chili-100kPMLP
F1-score (Weighted): 0.191 +/- 0.000
atomic-number-classification-on-chili-100kGIN
F1-score (Weighted): 0.336 +/- 0.005
atomic-number-classification-on-chili-100kGraphSAGE
F1-score (Weighted): 0.195 +/- 0.007
atomic-number-classification-on-chili-100kGraphUNet
F1-score (Weighted): 0.287 +/- 0.004
atomic-number-classification-on-chili-100kGCN
F1-score (Weighted): 0.275 +/- 0.002
atomic-number-classification-on-chili-100kEdgeCNN
F1-score (Weighted): 0.572 +/- 0.017
atomic-number-classification-on-chili-100kRandom
F1-score (Weighted): 0.015 +/- 0.000
atomic-number-classification-on-chili-100kMost Frequent Class
F1-score (Weighted): 0.192
atomic-number-classification-on-chili-3kRandom
F1-score (Weighted): 0.016 +/- 0.000
atomic-number-classification-on-chili-3kGIN
F1-score (Weighted): 0.587 +/- 0.002
atomic-number-classification-on-chili-3kPMLP
F1-score (Weighted): 0.461 +/- 0.000
atomic-number-classification-on-chili-3kEdgeCNN
F1-score (Weighted): 0.632 +/- 0.009
atomic-number-classification-on-chili-3kGCN
F1-score (Weighted): 0.496 +/- 0.001
atomic-number-classification-on-chili-3kGraphUNet
F1-score (Weighted): 0.552 +/- 0.079
atomic-number-classification-on-chili-3kMost Frequent Class
F1-score (Weighted): 0.461
atomic-number-classification-on-chili-3kGraphSAGE
F1-score (Weighted): 0.491 +/- 0.004
atomic-number-classification-on-chili-3kGAT
F1-score (Weighted): 0.461 +/- 0.000
crystal-system-classification-on-chili-100kGIN
F1-score (Weighted): 0.069 +/- 0.040
crystal-system-classification-on-chili-100kGraphSAGE
F1-score (Weighted): 0.061 +/- 0.019
crystal-system-classification-on-chili-100kGAT
F1-score (Weighted): 0.110 +/- 0.029
crystal-system-classification-on-chili-100kMost Frequent Class
F1-score (Weighted): 0.046
crystal-system-classification-on-chili-100kGCN
F1-score (Weighted): 0.069 +/- 0.023
crystal-system-classification-on-chili-100kGraphUNet
F1-score (Weighted): 0.068 +/- 0.006
crystal-system-classification-on-chili-100kPMLP
F1-score (Weighted): 0.124 +/- 0.036
crystal-system-classification-on-chili-100kEdgeCNN
F1-score (Weighted): 0.072 +/- 0.047
crystal-system-classification-on-chili-100kRandom
F1-score (Weighted): 0.168 +/- 0.014
crystal-system-classification-on-chili-3kGAT
F1-score (Weighted): 0.504 +/- 0.076
crystal-system-classification-on-chili-3kRandom
F1-score (Weighted): 0.191 +/- 0.008
crystal-system-classification-on-chili-3kGCN
F1-score (Weighted): 0.367 +/- 0.127
crystal-system-classification-on-chili-3kPMLP
F1-score (Weighted): 0.440 +/- 0.036
crystal-system-classification-on-chili-3kGIN
F1-score (Weighted): 0.438 +/- 0.004
crystal-system-classification-on-chili-3kGraphSAGE
F1-score (Weighted): 0.422 +/- 0.037
crystal-system-classification-on-chili-3kEdgeCNN
F1-score (Weighted): 0.657 +/- 0.196
crystal-system-classification-on-chili-3kGraphUNet
F1-score (Weighted): 0.431 +/- 0.014
crystal-system-classification-on-chili-3kMost Frequent Class
F1-score (Weighted): 0.440
distance-regression-on-chili-100kGAT
MSE : 0.252 +/- 0.003
distance-regression-on-chili-100kMean
MSE : 0.307
distance-regression-on-chili-100kGCN
MSE : 0.090 +/- 0.002
distance-regression-on-chili-100kEdgeCNN
MSE : 0.030 +/- 0.001
distance-regression-on-chili-100kPMLP
MSE : 0.486 +/- 0.014
distance-regression-on-chili-100kGraphUNet
MSE : 0.085 +/- 0.002
distance-regression-on-chili-100kGIN
MSE : 0.491 +/- 0.038
distance-regression-on-chili-100kGraphSAGE
MSE : 0.064 +/- 0.001
distance-regression-on-chili-3kGCN
MSE : 0.056 +/- 0.006
distance-regression-on-chili-3kGAT
MSE : 0.342 +/- 0.117
distance-regression-on-chili-3kPMLP
MSE : 0.359 +/- 0.017
distance-regression-on-chili-3kGraphUNet
MSE : 0.055 +/- 0.001
distance-regression-on-chili-3kGraphSAGE
MSE : 0.055 +/- 0.002
distance-regression-on-chili-3kEdgeCNN
MSE : 0.015 +/- 0.001
distance-regression-on-chili-3kMean
MSE : 0.265
distance-regression-on-chili-3kGIN
MSE : 0.464 +/- 0.005
position-regression-on-chili-100kGAT
Positional MAE: 16.336 +/- 0.000
position-regression-on-chili-100kGraphSAGE
Positional MAE: 16.337 +/- 0.000
position-regression-on-chili-100kGraphUNet
Positional MAE: 14.824 +/- 0.315
position-regression-on-chili-100kGIN
Positional MAE: 16.336 +/- 0.000
position-regression-on-chili-100kEdgeCNN
Positional MAE: 16.336 +/- 0.000
position-regression-on-chili-100kGCN
Positional MAE: 16.336 +/- 0.000
position-regression-on-chili-100kMean
Positional MAE: 16.336
position-regression-on-chili-100kPMLP
Positional MAE: 16.336 +/- 0.000
position-regression-on-chili-3kGCN
Positional MAE: 16.575 +/- 0.000
position-regression-on-chili-3kGraphSAGE
Positional MAE: 16.575 +/- 0.000
position-regression-on-chili-3kGIN
Positional MAE: 16.575 +/- 0.000
position-regression-on-chili-3kMean
Positional MAE: 16.575
position-regression-on-chili-3kGAT
Positional MAE: 16.575 +/- 0.000
position-regression-on-chili-3kGraphUNet
Positional MAE: 14.765 +/- 0.395
position-regression-on-chili-3kEdgeCNN
Positional MAE: 16.575 +/- 0.000
position-regression-on-chili-3kPMLP
Positional MAE: 16.575 +/- 0.000
saxs-regression-on-chili-100kEdgeCNN
MSE : 0.007 +/- 0.009
saxs-regression-on-chili-100kGIN
MSE : 0.009 +/- 0.000
saxs-regression-on-chili-100kGCN
MSE : 0.010 +/- 0.000
saxs-regression-on-chili-100kGraphSAGE
MSE : 0.011 +/- 0.002
saxs-regression-on-chili-100kMean
MSE : 0.038
saxs-regression-on-chili-100kPMLP
MSE : 0.003 +/- 0.000
saxs-regression-on-chili-100kGAT
MSE : 0.009 +/- 0.000
saxs-regression-on-chili-100kGraphUNet
MSE : 0.009 +/- 0.000
saxs-regression-on-chili-3kGIN
MSE : 0.008 +/- 0.000
saxs-regression-on-chili-3kGAT
MSE : 0.008 +/- 0.000
saxs-regression-on-chili-3kGraphUNet
MSE : 0.008 +/- 0.000
saxs-regression-on-chili-3kPMLP
MSE : 0.022 +/- 0.025
saxs-regression-on-chili-3kEdgeCNN
MSE : 0.006 +/- 0.004
saxs-regression-on-chili-3kGCN
MSE : 0.008 +/- 0.000
saxs-regression-on-chili-3kMean
MSE : 0.037
saxs-regression-on-chili-3kGraphSAGE
MSE : 0.008 +/- 0.001
space-group-classification-on-chili-100kPMLP
F1-score (Weighted): 0.047 +/- 0.012
space-group-classification-on-chili-100kMost Frequent Class
F1-score (Weighted): 0.010
space-group-classification-on-chili-100kGraphSAGE
F1-score (Weighted): 0.044 +/- 0.002
space-group-classification-on-chili-100kRandom
F1-score (Weighted): 0.002 +/- 0.001
space-group-classification-on-chili-100kGAT
F1-score (Weighted): 0.044 +/- 0.001
space-group-classification-on-chili-100kEdgeCNN
F1-score (Weighted): 0.158 +/- 0.035
space-group-classification-on-chili-100kGCN
F1-score (Weighted): 0.043 +/- 0.001
space-group-classification-on-chili-100kGIN
F1-score (Weighted): 0.043 +/- 0.000
space-group-classification-on-chili-100kGraphUNet
F1-score (Weighted): 0.043 +/- 0.000
space-group-classification-on-chili-3kGIN
F1-score (Weighted): 0.125 +/- 0.026
space-group-classification-on-chili-3kPMLP
F1-score (Weighted): 0.135 +/- 0.006
space-group-classification-on-chili-3kMost Frequent Class
F1-score (Weighted): 0.108
space-group-classification-on-chili-3kGraphUNet
F1-score (Weighted): 0.095 +/- 0.036
space-group-classification-on-chili-3kGAT
F1-score (Weighted): 0.113 +/- 0.013
space-group-classification-on-chili-3kEdgeCNN
F1-score (Weighted): 0.733 +/- 0.207
space-group-classification-on-chili-3kGCN
F1-score (Weighted): 0.099 +/- 0.019
space-group-classification-on-chili-3kRandom
F1-score (Weighted): 0.009 +/- 0.008
space-group-classification-on-chili-3kGraphSAGE
F1-score (Weighted): 0.151 +/- 0.045
x-ray-pdf-regression-on-chili-100kPMLP
MSE : 0.013 +/- 0.000
x-ray-pdf-regression-on-chili-100kGCN
MSE : 0.014 +/- 0.000
x-ray-pdf-regression-on-chili-100kGraphSAGE
MSE : 0.037 +/- 0.026
x-ray-pdf-regression-on-chili-100kGAT
MSE : 0.013 +/- 0.000
x-ray-pdf-regression-on-chili-100kGraphUNet
MSE : 0.013 +/- 0.000
x-ray-pdf-regression-on-chili-100kGIN
MSE : 0.013 +/- 0.000
x-ray-pdf-regression-on-chili-100kMean
MSE : 0.007
x-ray-pdf-regression-on-chili-100kEdgeCNN
MSE : 0.012 +/- 0.000
x-ray-pdf-regression-on-chili-3kGAT
MSE : 0.029 +/- 0.030
x-ray-pdf-regression-on-chili-3kGCN
MSE : 0.012 +/- 0.000
x-ray-pdf-regression-on-chili-3kMean
MSE : 0.008
x-ray-pdf-regression-on-chili-3kPMLP
MSE : 0.012 +/- 0.000
x-ray-pdf-regression-on-chili-3kGraphUNet
MSE : 0.012 +/- 0.000
x-ray-pdf-regression-on-chili-3kEdgeCNN
MSE : 0.011 +/- 0.000
x-ray-pdf-regression-on-chili-3kGraphSAGE
MSE : 0.012 +/- 0.000
xrd-regression-on-chili-100kMean
MSE : 0.021
xrd-regression-on-chili-100kEdgeCNN
MSE : 0.006 +/- 0.000
xrd-regression-on-chili-100kGraphSAGE
MSE : 0.018 +/- 0.014
xrd-regression-on-chili-100kGIN
MSE : 0.009 +/- 0.000
xrd-regression-on-chili-100kGraphUNet
MSE : 0.009 +/- 0.000
xrd-regression-on-chili-100kPMLP
MSE : 0.008 +/- 0.001
xrd-regression-on-chili-100kGCN
MSE : 0.009 +/- 0.000
xrd-regression-on-chili-100kGAT
MSE : 0.108 +/- 0.172
xrd-regression-on-chili-3kGraphUNet
MSE : 0.010 +/- 0.000
xrd-regression-on-chili-3kEdgeCNN
MSE : 0.008 +/- 0.001
xrd-regression-on-chili-3kMean
MSE : 0.017
xrd-regression-on-chili-3kGraphSAGE
MSE : 0.010 +/- 0.000
xrd-regression-on-chili-3kGAT
MSE : 0.010 +/- 0.000
xrd-regression-on-chili-3kPMLP
MSE : 0.010 +/- 0.000
xrd-regression-on-chili-3kGCN
MSE : 0.010 +/- 0.000

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CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning | Papers | HyperAI