Fig. 3: Overview of the accuracy-efficiency trade-offs of the proposed AdsorbML methods across several baseline GNN models. | npj Computational Materials

Fig. 3: Overview of the accuracy-efficiency trade-offs of the proposed AdsorbML methods across several baseline GNN models.

From: AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials

Fig. 3

For each model, DFT speedup and corresponding success rate are plotted for ML+RX and ML+SP across various best k. A system is considered successful if the predicted adsorption energy is within 0.1 eV of the DFT minimum, or lower. All success rates and speedups are relative to Random+Heuristic DFT. Heuristic DFT is shown as a common community baseline. The upper right-hand corner represents the optimal region—maximizing speedup and success rate. The point highlighted in teal corresponds to the balanced option reported in the abstract—a 87.36% success rate and 2290x speedup. A similar figure for the OC20-Dense validation set can be found in the SI.

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