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Accelerated design of novel hard coating materials requires state-of-the-artcomputational tools, which include data-driven techniques, building databases,and training machine learning models against the databases. In this work, wepresent a development of a heavily automated high-throughput workflow to builda database of industrially relevant hard coating materials, such as binary andternary nitrides. We use Vienna Ab initio Simulation package as the densityfunctional theory calculator and the high-throughput toolkit to automate thecalculation workflow. We calculate and present results, including the elasticconstants, one of the key materials parameter that determines mechanicalproperties of the coatings, for X(1-x)Y(x)N binary and ternary nitrides, whereX,Y in {Al, Ti, Zr, Hf} and fraction x = 0, 1/4, 1/2, 3/4, 1. We explore waysfor ML techniques to support and complement the designed databases. We findthat the crystal graph convolutional neural network model trained on MaterialsProject data for ordered lattices has sufficient prediction accuracy for thedisordered nitrides, suggesting that the existing databases provide importantdata for predicting mechanical properties of qualitatively different type ofmaterial systems, in our case hard coating alloys, not included in the originaldataset.

Predicting properties of hard-coating alloys using ab-initio and machine learning methods