摘要
Interstitial doping is a common approach to improve the mechanical or functional properties of high-entropy alloys (HEAs); their stability is usually predicted by a specific single descriptor. Herein, we consider six types of microstructure-based descriptor, seven types of electronic-structure-based local-environment descriptor and their combinations to predict the stability of the C- or N-doped VNbMoTaWTiAl0.5 (BCC) HEA, mainly using density functional theory (DFT) calculations. A machine-learning interatomic potential and Monte Carlo simulations were employed to verify the short-range order in the HEA. The microstructure-based descriptors include the composition of the first-, second-, and third-nearest neighbour shells (1NN, 2NN and 3NN), OctaDist distortion parameters (zeta, Delta, Sigma, Theta), the Voronoi volume (VVoronoi) of the dopant, and the volume change of the unit cell after doping (Delta Vcell); the electronic-structure-based local-environment descriptors include the local potential (LP), the electrostatic potential (EP), the charge density (CHG), the electron localization function (ELF) at the vacant doping site, the d-band center (epsilon d), the mean electronegativity (EN) of the 1NN shell around the dopant, and the Bader charge of the C or N dopants. For a single descriptor, the best correlation between the descriptor and the doping energy (indication of HEA stability) is found for 1NN with coefficient of determination (Q2) values of similar to 51 or similar to 61% obtained using the LOOCV (leave-one-out cross-validation) approach for C or N doping, respectively. After adding volume descriptor(s) into the linear regression model with the 1NN descriptor, Q2 increases to 72 and 76% for C and N doping, respectively. After further adding the electronic-structure-based EP descriptor, Q2 further increases to 75 and 80% for C and N doping, respectively, despite the poor correlation using a single volume descriptor. This study quantitatively combined and compared the independent contributions of different types of local-environment descriptors to the stability of the C- or N-doped HEA, demonstrating the importance of considering both key microstructure-based and electronic-structure-based local-environment descriptors using the regression models to achieve more accurate correlation of dopant stability in HEA; these combined approaches could be further applied to other materials systems, research fields and applications.