An ensemble machine‐learning method is demonstrated to be capable of finding 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 are extracted from the literature and used to train a supervised machine‐learning algorithm utilizing boosting, achieving excellent accuracy (R2 = 0.97). This new model is then tested by synthesizing and measuring the load‐dependent hardness of several unreported disilicides and analyzing the predicted hardness of several classic superhard materials. The trained ensemble method is then employed to screen for superhard materials by examining more than 66 000 compounds in crystal structure databases, which show that 68 known materials have a Vickers hardness ≥40 GPa at 0.5 N (applied force) and only 10 exceed this mark at 5 N. The hardness model is then combined with the data‐driven phase diagram generation tool to expand the limited number of reported high hardness compounds. Eleven ternary borocarbide phase spaces are studied, and more than ten thermodynamically favorable compositions with a hardness above 40 GPa (at 0.5 N) are identified, proving this ensemble model's ability to find previously unknown materials with outstanding mechanical properties.
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
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.
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