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Keyword: Matbench ×Clear all

2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

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

   Dunn, Wang, Ganose, Dopp and Jain · npj Computational Materials · 2020

   The authors present Matbench, a 13-task benchmark for predicting inorganic materials properties, and Automatminer, an automated machine learning pipeline, and compare it with other models.

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   1. UncheckedIn the authors' tests, the Automatminer pipeline gave the best results on 8 of the 13 tasks in the Matbench materials-property benchmark.“We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark.”
   2. UncheckedMatbench appears to show that crystal graph methods outperform traditional machine learning methods on materials property tasks with about 10,000 or more data points.“We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points.”