10 citations · 17 across the 5 of their papers we have counts for
6 papers
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…
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…
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…
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…
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…
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…