桂林电子科技大学学报Issue(5)：441-451,11.DOI:10.16725/j.1673-808X.2023124
基于机器学习的反尖晶石氧化物形成能研究
Research on the formation energy of inverse spinel oxides based on machine learning
摘要
Abstract
To fully utilize the data in open-source databases and expedite research on inverse spinel oxides,compound data with 1 746 chemical formulas of AB2O4 were extracted from the Materials Project(MP)database.From this dataset,111 inverse spinel oxides were selected,and a feature engineering approach based on cation occupancy was concurrently developed.The resulting data set was used as input variables for machine learning models to predict the formation energies of inverse spinel oxides.Three ma-chine learning models,random forest(RF),gradient boosting regression tree(GBRT),and extreme gradient boosting(XGBoost)were constructed using these algorithms.After performing feature selection,the optimal features influencing the models were identi-fied.Additionally,a comparison was made between the predicted formation energies and the actual values of inverse spinel oxides,the GBRT model that can provide the best predictions was obtained,and which achieves a coefficient of determination(R2)of 0.886667 and a root-mean-square error(RMSE)of 0.209 970 on the test set.The trained GBRT model can be utilized to predict the forma-tion energies of inverse spinel oxides,thus expediting the exploration progress of these materials.关键词
机器学习/密度泛函理论/反尖晶石氧化物/数据挖掘/形成能/特征工程Key words
machine learning/density functional theory/inverse spinel oxides/data mining/formation energy/feature engineering分类
数理科学引用本文复制引用
李子源,黄鹏儒,孙立贤,徐芬..基于机器学习的反尖晶石氧化物形成能研究[J].桂林电子科技大学学报,2024,(5):441-451,11.基金项目
国家重点研发计划(2021YFB3802400) （2021YFB3802400）
国家自然科学基金(52161037,U20A20237,51871065,52271205,51971068) （52161037,U20A20237,51871065,52271205,51971068）
桂林市科技计划(20210102-4,20210216-1) （20210102-4,20210216-1）
广西科技计划(AA19182014,AD17195073,AA17202030-1,AB21220027) （AA19182014,AD17195073,AA17202030-1,AB21220027）
广西先进功能材料基础与应用人才小高地和中德科学中心国际交流合作项目(GZ1528) （GZ1528）
桂林电子科技大学研究生教育创新计划(2022YCXS197) （2022YCXS197）
