Machine Learning Directed Search for Ultraincompressible, Superhard Materials.
By Aria Mansouri Tehrani, A. Oliynyk, M. Parry, Zeshan Rizvi, S. Couper, F. Lin, L. Miyagi, Taylor D. Sparks, Jakoah Brgoch. 2018
274 citations recorded by the source corpus.
In the pursuit of materials with exceptional mechanical properties, a machine-learning model is developed to direct the synthetic efforts toward compounds with high hardness by predicting the elastic moduli as a proxy. This approach screens 118 287 compounds compiled in crystal structure databases for the materials with the highest bulk and shear moduli determined by support vector machine regression. Following these models, a ternary rhenium tungsten carbide and a quaternary molybdenum tungsten borocarbide are selected and synthesized at ambient pressure. High-pressure diamond anvil cell measurements corroborate the machine-learning prediction of the bulk modulus with less than 10% error, as well as confirm the ultraincompressible nature of both compounds. Subsequent Vickers microhardness measurements reveal that each compound also has an extremely high hardness exceeding the superhard threshold of 40 GPa at low loads (0.49 N). These results show the effectiveness of materials development through state-of-the-art machine-learning techniques by identifying functional inorganic materials.
Subfields: Materials Chemistry, Mechanical Engineering
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In Materials Chemistry
Citations in the corpus
- Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm (2020, methodology): “For example, by using support vector machines to search a space of more than 118k candidate crystal structures, Tehrani et al.3 identified, synthesized, and experimentally validated two superhard carbides.”