Abstract
We build random forests models to predict elastic properties and mechanical hardness of a compound, using only its chemical formula as input. The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, and ionic bonding levels. Using the models, we construct triangular graphs for B-C-N compounds to map out their bulk and shear moduli, as well as hardness values. The graphs indicate that a 1:1 B-N ratio can lead to various superhard compositions. We also validate the machine learning results by evolutionary structure prediction and density functional theory. Our study shows that BC10N, B4C5N3, and B2C3N exhibit dynamically stable phases with hardness values >40 GPa, which are superhard materials that potentially could be synthesized by low-temperature plasma methods.
Highlights
Superhard materials exhibit a Vickers hardness H ≥ 40 GPa, and they have extensive applications such as abrasives, cutting tools, and protective coatings[1,2,3,4,5]
First-principles simulations based on density functional theory (DFT) have played important roles in predicting superhard compounds
10,421 samples based on density functional theory (DFT) calculations
Summary
Superhard materials exhibit a Vickers hardness H ≥ 40 GPa, and they have extensive applications such as abrasives, cutting tools, and protective coatings[1,2,3,4,5]. Meredig et al.[31] have constructed a machine learning model to screen over 1.6 million ternary compositions and predicted 4500 potentially stable ternary materials. We develop random forests models to predict material mechanical properties, by using only features that can be derived directly from the chemical formula.
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