Machine learning guided discovery of super-hard high entropy ceramics

被引：30

作者：

Jaafreh, Russlan ^{[1
]}

Kang, Yoo Seong ^{[1
,2
]}

Kim, Jung-Gu ^{[1
]}

Hamad, Kotiba ^{[1
]}

机构：

[1] Sungkyunkwan Univ, Sch Adv Mat Sci & Engn, Suwon 16419, South Korea

[2] Sungkyunkwan Univ, Sch Appl Data Sci, Suwon 16419, South Korea

来源：

MATERIALS LETTERS | 2022年 / 306卷

基金：

新加坡国家研究基金会;

关键词：

High entropy ceramics; Hardness; Machine learning; Composition features;

D O I：

10.1016/j.matlet.2021.130899

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In the present work, we used machine learning (ML) to explore new compositions of high entropy ceramic (HEC) materials that show improved hardness. Starting from a dataset containing hardness, loads and compositions of 557 ceramic materials including HECs, a ML model was built using random forest (RF) algorithm. The RF-based model successfully reproduced experimental load-hardness behavior of Al2O3, (Hf0.2Zr0.2Ti0.2Ta0.2Nb0.2)B-2 and (Hf0.2Zr0.2Ti0.2Ta0.2Mo0.2)B-2. Accordingly, the built model was employed to find super-hard HECs.

引用

页数：5

共 65 条

[1] Investigation of Hardness of Ternary Borides of the YCrB4, Y2ReB6, Y3ReB7, and YMo3B7 Structural Types [J].