First-principles investigation of local atomic environment and its impact on properties in non-equiatomic CoCrFeNi high-entropy alloys

First-principles investigation of local atomic environment and its impact on properties in non-equiatomic CoCrFeNi high-entropy alloys

Employing first-principles calculations, the local atomic environment (LAE), including local lattice distortion (LLD), charge transfer effect (CTE), and short-range order (SRO), was investigated in 105 sets of non-equiatomic CoCrFeNi high-entropy alloys (HEAs). A predictive model for lattice constan...

Full description

Saved in:
Bibliographic Details
Main Authors: Qingxu Tian, Fuhui Chen, Peter K. Liaw, Yang Tong, Shuying Chen, Fanchao Meng
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Journal of Materials Research and Technology
Subjects:
High-entropy alloys
Local lattice distortion
Charge transfer effect
Short-range order
Machine learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2238785425000018
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Employing first-principles calculations, the local atomic environment (LAE), including local lattice distortion (LLD), charge transfer effect (CTE), and short-range order (SRO), was investigated in 105 sets of non-equiatomic CoCrFeNi high-entropy alloys (HEAs). A predictive model for lattice constant vs. alloy composition was established. The results reveal a strong correlation between the total energy and atomic percentage of Cr + Fe atomic pairs, with higher concentrations of Cr + Fe resulting in lower total energies, which relates closely to their tendency to form the SRO. The LLD is found to be heavily influenced by the chemical composition, with Cr–Cr and Cr–Fe atomic pairs making significant contributions. The CTE analysis indicates that Ni and Cr tend to gain charge, while Co tends to lose charge, leading to modifications in their atomic radii. The SRO analysis suggests that Cr–Fe, Fe–Co, and Cr–Co atomic pairs are highly likely to form short-range ordering. The microhardness exhibits distinct dependencies on the relative concentrations of the constituent elements. Machine learning (ML) techniques were employed to establish a functional relationship between the LAE and microhardness, revealing the significant impact of LAE on the mechanical properties of non-equiatomic CoCrFeNi HEAs.
ISSN:2238-7854

Similar Items