Machine Learning and Evolutionary Prediction of Superhard B-C-N Compounds

We build random forests models to predict mechanical properties of a compound, using only its chemical formula as input. The model is trained with over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, and ionic bonding levels. The model can achieve Pearson correlation coefficients > 0.9 for bulk and shear modulus regressions. Using the model, 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 apply the machine learning models to search new superhard compounds, and validate the results using evolutionary structure prediction and density functional theory. We discover several dynamically stable phases of B-C-N compounds with hardness values > 40GPa, which are potentially new superhard materials that could be synthesized by low-temperature plasma methods.