Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science

Ai Koizumi; Guillaume Deffrennes; Kei Terayama; Ryo Tamura

doi:10.1038/s41598-024-76800-4

論文 Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science

Ai Koizumi ; Guillaume Deffrennes ; Kei Terayama ; Ryo Tamura

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Ai Koizumi, Guillaume Deffrennes, Kei Terayama, Ryo Tamura. Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science. Scientific Reports. 2024, 14 (), 27019. https://doi.org/10.1038/s41598-024-76800-4

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Obtaining a fine approximation of a black-box function is important for understanding and evaluating innovative materials. Active learning aims to improve the approximation of black-box functions with fewer training data. In this study, we investigate whether active learning based on uncertainty sampling enables the efficient approximation of black-box functions in regression tasks using various material databases. In cases where the inputs are provided uniformly and defined in a relatively low-dimensional space, the liquidus surfaces of the ternary systems are the focus. The results show that uncertainty-based active learning can produce a better black-box function with higher prediction accuracy than that by random sampling. Furthermore, in cases in which the inputs are distributed discretely and unbalanced in a high-dimensional feature space, datasets extracted from materials databases for inorganic materials, small molecules, and polymers are addressed, and uncertainty-based active learning is occasionally inefficient. Based on the dependency on the material descriptors, active learning tends to produce a better black-box functions than random sampling when the dimensions of the descriptor are small. The results indicate that active learning is occasionally inefficient in obtaining a better black-box function in materials science.

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