Fig. 5: Designing an acquisition strategy for the specific goals of finding ternary compositions which satisfy multiband and wishlist goals. | npj Computational Materials

Fig. 5: Designing an acquisition strategy for the specific goals of finding ternary compositions which satisfy multiband and wishlist goals.

From: Targeted materials discovery using Bayesian algorithm execution

Fig. 5

The notation [[a, b], [c, d]] denotes a < Kerr Rotation (mrad) < b and c < coercivity (mT) < d. a Results for the following multiband: [[2.0, 3.0], [0.3, 0.4]]. b Results for the following wishlist: [[2.0,3.0], [0.2, 0.3]] or [[4.0,6.0], [0.2, 0.4]] or [[9.0, 10.0], [0.0, 0.1]] or [[3.0,4.0], [0.7, 0.8]]. The error bars characterize the robustness to different randomly chosen sets of initial data (one standard deviation computed over 20 repetitions with 10 initial datapoints). Design and property space sampling patterns are shown after 200 iterations for the SwitchBAX acquisition function. The BAX strategies show superior sampling profiles relative to RS, US and EHVI, underscoring the utility of user-directed algorithmic sampling.

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