有色金属材料与工程2026，Vol.47Issue(3)：12-18,7.DOI:10.13258/j.cnki.nmme.20260121001
金红石型高熵氧化物氢吸附的密度泛函理论计算与图神经网络预测模型
Density functional theory calculations and graph neural network prediction model for H2 adsorption on rutile-type high-entropy oxides
摘要
Abstract
Traditional semiconductor hydrogen sensors struggle to simultaneously achieve rapid response,high sensitivity,and high selectivity at low temperatures,thus failing to meet the urgent needs in the field of hydrogen energy safety.High-entropy oxides(HEOs)offer a new approach for designing efficient hydrogen-sensitive materials.Studies have reported that hydrogen atoms can enter the lattice of rutile-structured oxide single crystals at room temperature,leading to a decrease in resistance.This suggests that rutile-type HEOs have great potential for hydrogen adsorption applications.Targeting the multi-component disordered characteristics of rutile-type HEOs,this study generated a standardized structure library via the special quasirandom structure method.Combined with density functional theory calculations,the Zr-O bridge site was identified as the optimal adsorption site.A graph neural network model was constructed to predict the adsorption energy,achieving an R2 of 0.889,an root mean square error of 0.203 eV and an mean absolute error of 0.127 eV on the test set.This work provides a powerful theoretical tool for the high-throughput screening of hydrogen-sensitive HEOs materials.关键词
高熵氧化物/密度泛函理论计算/图神经网络/气体吸附Key words
high-entropy oxides/density functional theory calculations/graph neural network/gas adsorption分类
化学化工引用本文复制引用
蒋子棱,徐京城..金红石型高熵氧化物氢吸附的密度泛函理论计算与图神经网络预测模型[J].有色金属材料与工程,2026,47(3):12-18,7.基金项目
传感器技术全国重点实验室开放课题SKT2502 （）
