activity
20162022
most citedMachine learning modeling of high entropy alloy: the role of short-range order

10 citations · 29 across the 8 of their papers we have counts for

collaborators

8 papers

cond-mat.mtrl-sci2022

Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities

Xianglin Liu, Jiaxin Zhang, Zongrui Pei

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding th…

cond-mat.mtrl-sci2020★ 2 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-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-sci2019★ 9 cited

Robust data-driven approach for predicting the configurational energy of high entropy alloys

Jiaxin Zhang, Xianglin Liu, Sirui Bi +3

High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially charact…

cond-mat.mtrl-sci2019★ 5 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-sci2019★ 10 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…

cond-mat.mtrl-sci2017

Relativistic full-potential multiple scattering theory: An ab initio method and its applications

Xianglin Liu, Yang Wang, Markus Eisenbach +1

The Green function plays an essential role in the Kohn-Korringa-Rostocker (KKR) multiple scattering method. In practice, it is constructed from the regular and irregular solutions…

cond-mat.mtrl-sci2016★ 3 cited

A full-potential approach to the relativistic single-site Green's function

Xianglin Liu, Yang Wang, Markus Eisenbach +1

One major purpose of studying the single-site scattering problem is to obtain the scattering matrices and differential equation solutions indispensable to multiple scattering theor…