Fig. 1: Categorical dataset distribution of the 13 machine learning tasks in the Matbench test suite v0.1. | npj Computational Materials

Fig. 1: Categorical dataset distribution of the 13 machine learning tasks in the Matbench test suite v0.1.

From: Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Fig. 1

Methods of categorization are listed on the left: Application describes the ML target property of the task as it relates to materials, Num. samples describes the number of samples in each task, Input Type describes the materials primitives that serve as input for each task, and Task Type designates the supervised ML task type. Numbers in the bars represent the number of tasks fitting the descriptor above it (e.g., there are 10 regression tasks).

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