scholarly journals Predicting superhard materials via a machine learning informed evolutionary structure search

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Patrick Avery ◽  
Xiaoyu Wang ◽  
Corey Oses ◽  
Eric Gossett ◽  
Davide M. Proserpio ◽  
...  
2022 ◽  
Vol 35 ◽  
pp. 100771
Author(s):  
Eric Musa ◽  
Francis Doherty ◽  
Bryan R Goldsmith

Author(s):  
Mohammed Al-Fahdi ◽  
Tao Ouyang ◽  
Ming Hu

Novel carbon allotropes and ternary B–C–N structures with ultrahigh hardness were screened and proposed by high-throughput computation. Electronic-level insights into superhard materials were provided from machine learning.


2020 ◽  
Vol 7 (15) ◽  
pp. 2000992
Author(s):  
Alexander T. Egger ◽  
Lukas Hörmann ◽  
Andreas Jeindl ◽  
Michael Scherbela ◽  
Veronika Obersteiner ◽  
...  

2020 ◽  
Author(s):  
Ziyan Zhang ◽  
Aria Mansouri Tehrani ◽  
Anton Oliynyk ◽  
Blake Day ◽  
Jakoah Brgoch

We report an ensemble machine-learning method capable of finding new superhard materials by directly predicting the load-dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load-dependent Vickers hardness data were extracted from the literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving excellent accuracy (R2 = 0.97). This new model was then tested by synthesizing and measuring the load-dependent hardness of several unreported disilicides as well as analyzing the predicted hardness of several classic superhard materials. The trained ensemble method was then employed to screen for superhard materials by examining more than 66,000 compounds in crystal structure databases, which showed that only 68 known materials surpass the superhard threshold. The hardness model was then combined with our data-driven phase diagram generation tool to expand the limited num1 ber of reported compounds. Eleven ternary borocarbide phase spaces were studied, and more than ten thermodynamically favorable compositions with superhard potential were identified, proving this ensemble model’s ability to find previously unknown superhard materials


2020 ◽  
Author(s):  
Ziyan Zhang ◽  
Aria Mansouri Tehrani ◽  
Anton Oliynyk ◽  
Blake Day ◽  
Jakoah Brgoch

We report an ensemble machine-learning method capable of finding new superhard materials by directly predicting the load-dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load-dependent Vickers hardness data were extracted from the literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving excellent accuracy (R2 = 0.97). This new model was then tested by synthesizing and measuring the load-dependent hardness of several unreported disilicides as well as analyzing the predicted hardness of several classic superhard materials. The trained ensemble method was then employed to screen for superhard materials by examining more than 66,000 compounds in crystal structure databases, which showed that only 68 known materials surpass the superhard threshold. The hardness model was then combined with our data-driven phase diagram generation tool to expand the limited num1 ber of reported compounds. Eleven ternary borocarbide phase spaces were studied, and more than ten thermodynamically favorable compositions with superhard potential were identified, proving this ensemble model’s ability to find previously unknown superhard materials


2018 ◽  
Vol 63 (13) ◽  
pp. 817-824 ◽  
Author(s):  
Kang Xia ◽  
Hao Gao ◽  
Cong Liu ◽  
Jianan Yuan ◽  
Jian Sun ◽  
...  

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