Skip to main content

Table 3 Comparison of prediction and uncertainty quantification performance on out-of-sample splits of Buchwald-Hartwig dataset

From: Uncertainty-aware prediction of chemical reaction yields with graph neural networks

Out-of-sample split Measure YieldBERT YieldBERT-DA Proposed (\(\lambda = 0.1\))
Test 1 MAE (%p) 7.351 ± 0.099 \(\mathbf{7.015} \pm \mathbf{0.758}\) 8.082 ± 0.827
RMSE (%p) \(\mathbf{11.441} \pm \mathbf{0.342}\) 11.761 ± 1.398 13.746 ± 1.175
R\(^2\) \(\mathbf{0.824} \pm \mathbf{0.010}\) 0.811 ± 0.047 0.744 ± 0.042
Spearman \(\rho\) 0.380 ± 0.065 \(\mathbf{0.454} \pm \mathbf{0.046}\)
Test 2 MAE (%p) 7.266 ± 0.724 6.588 ± 0.328 \(\mathbf{6.300} \pm \mathbf{0.647}\)
RMSE (%p) 11.144 ± 1.267 9.886 ± 0.741 \(\mathbf{9.476} \pm \mathbf{1.027}\)
R\(^2\) 0.829 ± 0.037 0.866 ± 0.020 \(\mathbf{0.876} \pm \mathbf{0.026}\)
Spearman \(\rho\) \(\mathbf{0.494} \pm \mathbf{0.044}\) 0.397 ± 0.043
Test 3 MAE (%p) 9.129 ± 0.745 11.052 ± 0.950 \(\mathbf{8.986} \pm \mathbf{0.314}\)
RMSE (%p) \(\mathbf{14.276} \pm \mathbf{0.820}\) 18.041 ± 1.395 14.939 ± 0.622
R\(^2\) \(\mathbf{0.741} \pm \mathbf{0.030}\) 0.585 ± 0.067 0.717 ± 0.024
Spearman \(\rho\) 0.406 ± 0.065 \(\mathbf{0.423} \pm \mathbf{0.031}\)
Test 4 MAE (%p) 13.671 ± 1.067 18.422 ± 0.620 \(\mathbf{13.190} \pm \mathbf{0.754}\)
RMSE (%p) 19.679 ± 1.397 24.279 ± 0.494 \(\mathbf{18.774} \pm \mathbf{0.566}\)
R\(^2\) 0.444 ± 0.077 0.157 ± 0.034 \(\mathbf{0.496} \pm \mathbf{0.031}\)
Spearman \(\rho\) 0.366 ± 0.100 \(\mathbf{0.461} \pm \mathbf{0.040}\)