关键词: 密度泛函理论, 机器学习, 特征工程, “中心-环境”特征模型, 尖晶石氧化物

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