Engineering and Technology
Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm
A. Dunn, Q. Wang, et al.
This paper presents Matbench, a comprehensive benchmark suite for assessing machine learning models in predicting inorganic bulk materials properties, alongside Automatminer, an automated ML pipeline. Authored by Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain, the research highlights the superiority of Automatminer in 8 out of 13 tasks, showcasing its potential in material property predictions without user intervention.
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