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Machine Learning: Finding the Next Superhard Material through Ensemble Learning (Adv. Mater. 5/2021)
Author(s) -
Zhang Ziyan,
Mansouri Tehrani Aria,
Oliynyk Anton O.,
Day Blake,
Brgoch Jakoah
Publication year - 2021
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.202170034
Subject(s) - materials science , vickers hardness test , ensemble learning , alma mater , machine learning , composite material , computer science , microstructure , medicine , dura mater , radiology
In article number 2005112, Jakoah Brgoch and co‐workers establish an ensemble machine‐learning method to find new superhard materials. The model is trained on the sparse experimental data available in the literature to predict load‐dependent Vickers hardness based only on chemical composition. Crystal‐structure databases and unknown phase diagrams are then screened in search of novel materials with an outstanding mechanical response.