activity
20192021
most citedAn investigation of high entropy alloy conductivity using first-principles calculations

20 citations · 49 across the 6 of their papers we have counts for

collaborators
Showing cond-mat.mtrl-sciShow all

6 papers · 1 filter

cond-mat.mtrl-sci202120 cited

An investigation of high entropy alloy conductivity using first-principles calculations

Vishnu Raghuraman, Yang Wang, Michael Widom

The Kubo-Greenwood equation, in combination with the first-principles Korringa-Kohn-Rostoker Coherent Potential Approximation (KKR-CPA) can be used to calculate the DC residual res…

cond-mat.mtrl-sci20202 cited

Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach

Xianglin Liu, Jiaxin Zhang, Junqi Yin +3

High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is i…

cond-mat.mtrl-sci202012 cited

An averaged cluster approach to including chemical short range order in KKR-CPA

Vishnu Raghuraman, Yang Wang, Michael Widom

The single-site Korringa-Kohn-Rostoker Coherent Potential Approximation (KKR-CPA) ignores short range ordering present in disordered metallic systems. In this paper, we establish a…

cond-mat.mtrl-sci2019

Machine Learning the Effective Hamiltonian in High Entropy Alloys

Xianglin Liu, Jiaxin Zhang, Markus Eisenbach +1

The development of machine learning sheds new light on the problem of statistical thermodynamics in multicomponent alloys. However, a data-driven approach to construct the effectiv…

cond-mat.mtrl-sci20195 cited

Chemical complexity in high entropy alloys: A pair-interaction perspective

Xianglin Liu, Jiaxin Zhang, Sirui Bi +3

The recently proposed pair-interaction model is applied to study a series of refractory high entropy alloys. The results demonstrate the simplicity, robustness, and high accuracy o…

cond-mat.mtrl-sci201910 cited

Machine learning modeling of high entropy alloy: the role of short-range order

Xianglin Liu, Jiaxin Zhang, Markus Eisenbach +1

The development of machine learning sheds new light on the traditionally complicated problem of thermodynamics in multicomponent alloys. Successful application of such a method, ho…