Table 5 Comparison of selective prediction performance in terms of RMSE (%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% | 4.799 ± 0.261 | \(\mathbf{4.433} \pm \mathbf{0.085}\) | \(\mathbf{4.433} \pm \mathbf{0.085}\) | \(\mathbf{4.433} \pm \mathbf{0.085}\) |
90% | 4.129 ± 0.205 | 4.036 ± 0.130 | \(\mathbf{4.003} \pm \mathbf{0.160}\) | 4.037 ± 0.161 | |
80% | 3.833 ± 0.206 | 3.796 ± 0.173 | 3.793 ± 0.182 | \(\mathbf{3.765} \pm \mathbf{0.185}\) | |
70% | 3.583 ± 0.249 | 3.482 ± 0.176 | \(\mathbf{3.424} \pm \mathbf{0.196}\) | 3.456 ± 0.166 | |
60% | 3.382 ± 0.282 | 3.050 ± 0.261 | 3.068 ± 0.211 | \(\mathbf{3.001} \pm \mathbf{0.184}\) | |
50% | 3.171 ± 0.317 | 2.653 ± 0.187 | 2.716 ± 0.168 | \(\mathbf{2.605} \pm \mathbf{0.115}\) | |
40% | 2.812 ± 0.218 | 2.338 ± 0.178 | 2.503 ± 0.197 | \(\mathbf{2.300} \pm \mathbf{0.166}\) | |
30% | 2.518 ± 0.229 | 2.059 ± 0.245 | 2.299 ± 0.270 | \(\mathbf{2.044} \pm \mathbf{0.235}\) | |
Suzuki-Miyaura | 100% | 10.524 ± 0.482 | \(\mathbf{9.467} \pm \mathbf{0.459}\) | \(\mathbf{9.467} \pm \mathbf{0.459}\) | \(\mathbf{9.467} \pm \mathbf{0.459}\) |
90% | 9.485 ± 0.395 | 8.632 ± 0.334 | 8.592 ± 0.338 | \(\mathbf{8.540} \pm \mathbf{0.310}\) | |
80% | 8.911 ± 0.373 | 8.254 ± 0.314 | 8.146 ± 0.403 | \(\mathbf{8.098} \pm \mathbf{0.347}\) | |
70% | 8.473 ± 0.323 | 7.848 ± 0.329 | 7.787 ± 0.305 | \(\mathbf{7.702} \pm \mathbf{0.397}\) | |
60% | 8.063 ± 0.353 | 7.260 ± 0.400 | 7.218 ± 0.343 | \(\mathbf{7.160} \pm \mathbf{0.328}\) | |
50% | 7.439 ± 0.470 | 6.357 ± 0.470 | 6.503 ± 0.456 | \(\mathbf{6.293} \pm \mathbf{0.466}\) | |
40% | 7.236 ± 0.521 | 5.126 ± 0.306 | 5.394 ± 0.306 | \(\mathbf{4.980} \pm \mathbf{0.250}\) | |
30% | 6.754 ± 0.398 | 3.968 ± 0.152 | 4.337 ± 0.257 | 3.959 ± 0.252 | |