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

TL;DR

This paper develops an automated workflow to create a database of hard coating materials using ab-initio calculations and explores machine learning models to predict their elastic properties, aiding accelerated material design.

Contribution

It introduces a high-throughput computational workflow for hard coating alloys and demonstrates ML models can effectively predict properties of disordered nitrides using existing databases.

Findings

High-throughput workflow automates database creation for nitrides.

ML models trained on existing data predict properties of disordered alloys.

Elastic constants calculated for various binary and ternary nitrides.

Abstract

Accelerated design of novel hard coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine learning models against the databases. In this work, we present a development of a heavily automated high-throughput workflow to build a database of industrially relevant hard coating materials, such as binary and ternary nitrides. We use Vienna Ab initio Simulation package as the density functional theory calculator and the high-throughput toolkit to automate the calculation workflow. We calculate and present results, including the elastic constants, one of the key materials parameter that determines mechanical properties of the coatings, for X(1-x)Y(x)N binary and ternary nitrides, where X,Y in {Al, Ti, Zr, Hf} and fraction x = 0, 1/4, 1/2, 3/4, 1. We explore ways for ML techniques to support and complement the designed databases. We find that the crystal graph convolutional neural network model trained on Materials Project data for ordered lattices has sufficient prediction accuracy for the disordered nitrides, suggesting that the existing databases provide important data for predicting mechanical properties of qualitatively different type of material systems, in our case hard coating alloys, not included in the original dataset.