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

Journal: STAR protocols
Published Date: Jul 18, 2022

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).

Authors

  • Yunqing Tang
    Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 2H5, Canada. Electronic address: yunqing1@ualberta.ca.
  • Dong Zhang
    Institute of Acoustics, Nanjing University, Nanjing 210093, China.
  • Ruiliang Liu
    Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 2H5, Canada.
  • Dongyang Li
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

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