Table 4 Comparison of selective prediction performance in terms of MAE (%p)
From: Uncertainty-aware prediction of chemical reaction yields with graph neural networks
Dataset | Coverage | YieldBERT-DA | Proposed (\(\lambda = 0.1\)) | ||
|---|---|---|---|---|---|
Aleatoric | Epistemic | Total Pred. Var. | |||
Buchwald-Hartwig | 100% | 3.090 ± 0.118 | \(\mathbf{2.920} \pm \mathbf{0.056}\) | \(\mathbf{2.920} \pm \mathbf{0.056}\) | \(\mathbf{2.920} \pm \mathbf{0.056}\) |
90% | 2.733 ± 0.099 | 2.684 ± 0.050 | \(\mathbf{2.669} \pm \mathbf{0.056}\) | 2.683 ± 0.061 | |
80% | 2.534 ± 0.082 | 2.518 ± 0.064 | 2.514 ± 0.063 | \(\mathbf{2.505} \pm \mathbf{0.065}\) | |
70% | 2.357 ± 0.092 | 2.302 ± 0.067 | \(\mathbf{2.292} \pm \mathbf{0.067}\) | 2.293 ± 0.064 | |
60% | 2.191 ± 0.103 | 2.056 ± 0.099 | 2.070 ± 0.064 | \(\mathbf{2.041} \pm \mathbf{0.069}\) | |
50% | 2.020 ± 0.105 | 1.820 ± 0.093 | 1.847 ± 0.075 | \(\mathbf{1.803} \pm \mathbf{0.061}\) | |
40% | 1.824 ± 0.106 | 1.593 ± 0.086 | 1.672 ± 0.081 | \(\mathbf{1.582} \pm \mathbf{0.077}\) | |
30% | 1.560 ± 0.098 | \(\mathbf{1.368} \pm \mathbf{0.112}\) | 1.509 ± 0.115 | 1.372 ± 0.111 | |
Suzuki-Miyaura | 100% | 6.598 ± 0.270 | \(\mathbf{6.116} \pm \mathbf{0.223}\) | \(\mathbf{6.116} \pm \mathbf{0.223}\) | \(\mathbf{6.116} \pm \mathbf{0.223}\) |
90% | 5.902 ± 0.247 | 5.589 ± 0.178 | 5.575 ± 0.191 | \(\mathbf{5.542} \pm \mathbf{0.178}\) | |
80% | 5.415 ± 0.242 | 5.298 ± 0.174 | 5.269 ± 0.210 | \(\mathbf{5.219} \pm \mathbf{0.192}\) | |
70% | 5.052 ± 0.211 | 5.018 ± 0.196 | 4.966 ± 0.183 | \(\mathbf{4.939} \pm \mathbf{0.208}\) | |
60% | 4.690 ± 0.181 | 4.641 ± 0.218 | 4.579 ± 0.140 | \(\mathbf{4.570} \pm \mathbf{0.188}\) | |
50% | 4.213 ± 0.214 | 4.025 ± 0.252 | 4.064 ± 0.179 | \(\mathbf{3.989} \pm \mathbf{0.203}\) | |
40% | 3.921 ± 0.188 | 3.245 ± 0.140 | 3.372 ± 0.111 | \(\mathbf{3.195} \pm \mathbf{0.145}\) | |
30% | 3.549 ± 0.120 | \(\mathbf{2.510} \pm \mathbf{0.093}\) | 2.701 ± 0.118 | 2.514 ± 0.115 | |