Protocol to predict mechanical properties of multi-element ceramics using machine learning

Yunqing Tang; Dong Zhang; Ruiliang Liu; Dongyang Li

doi:10.1016/j.xpro.2022.101552

Protocol to predict mechanical properties of multi-element ceramics using machine learning

STAR Protoc. 2022 Sep 16;3(3):101552. doi: 10.1016/j.xpro.2022.101552. Epub 2022 Jul 18.

Authors

Yunqing Tang¹, Dong Zhang², Ruiliang Liu², Dongyang Li³

Affiliations

¹ Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 2H5, Canada. Electronic address: yunqing1@ualberta.ca.
² Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 2H5, Canada.
³ Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 2H5, Canada. Electronic address: dongyang.li@ualberta.ca.

Abstract

Identifying and designing high-performance multi-element ceramics based on trial-and-error approaches are ineffective and expensive. Here, we present a machine-learning-accelerated method for prediction of mechanical properties of multi-element ceramics, based on the density functional theory calculation database. Specific bonding characteristics are used as highly efficient machine learning descriptors. This protocol describes a low-cost, high-efficiency, and reliable workflow for developing advanced ceramics with superior mechanical properties. For complete details on the use and execution of this protocol, please refer to Tang et al. (2021).

Keywords: Computer sciences; Material sciences; Physics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Ceramics*
Machine Learning*