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Journal of Shanghai University >

2021 , Vol. 27 >Issue 4: 635 - 649

DOI: https://doi.org/10.12066/j.issn.1007-2861.2251

Research Articles

First-principles computation and machine learning of the energies and structures of spinel oxides

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  • 1. College of Sciences, Shanghai University, Shanghai 200444, China
    2. Materials Genome Institute, Shanghai University, Shanghai 200444, China

Received date: 2020-08-10

  Online published: 2020-09-09

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Abstract

The formal spinel oxides AB$_{2}$O$_{4}$ can have 5 329 configurations by substituting A and B with 73 elements. The first-principles method was applied to calculate the formation energies and lattice constants of 5 329 spinel oxides. To develop efficient machine learning (ML) methods, centre-environment (CE) feature models were proposed to construct the input variables of the ML methods containing local composition and structure information. Based on the first-principles computational data, random forest algorithm was used to develop an ML model to predict the formation energies and lattice constants of spinel oxides. By comparing the formation energies of hypothetical and experimental structures predicted by ML, 361 new and more stable spinel oxides were discovered. The “good” and “bad” stabilisation elements were disscussed, which helped in guiding theexperimental synthesis of novel stable spinel oxides.

Key words： density functional theory (DFT); machine learning (ML); feature engineering; centre-environment (CE) feature model; spinel oxide

Cite this article

LI Yihang, XIAO Bin, TANG Yuchao, LIU Fu, WANG Xiaomeng, LIU Yi . First-principles computation and machine learning of the energies and structures of spinel oxides[J]. Journal of Shanghai University, 2021 , 27(4) : 635 -649 . DOI: 10.12066/j.issn.1007-2861.2251

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