fig10

High-entropy alloy catalysts: high-throughput and machine learning-driven design

Figure 10. Plots showing GPR-predicted (ΔEpred) versus DFT-calculated (ΔEDFT) adsorption energies for (A-C) CoCuGaNiZn and (D-F) AgAuCuPdPt for (A, D) on-top CO, (B, E) FCC-hollow H and (C, F) HCP-hollow H. Blue and red indicate data for 2 × 2 and 3 × 3 atom slabs. MAEs are calculated as a fivefold cross-validation prediction error for the 2 × 2 and 3 × 3 slabs as the prediction error when training on the set of all 2 × 2 slabs. The insets show the distribution of the prediction errors in eV defined as ΔEpred-ΔEDFT. (G) correlation matrix of all the input features and output (target) adsorption energy for CO*, where 1, 2and 3 represent the first, second and third regions of the microstructure, respectively. (A-F) Reproduced with permission[86]. Copyright 2020, American Chemical Society. (G) Reproduced with permission[53]. Copyright 2021, American Chemical Society. HCP: hexagonal-closed packed; FCC: face-centered cubic; MAE: mean absolute error.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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