34 citations · 61 across the 16 of their papers we have counts for
8 papers · 1 filter
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…
Fast and stable deep-learning predictions of material properties for solid solution alloys
Massimiliano Lupo Pasini, Ying Wai Li, Junqi Yin +3
We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The m…
Electron spin mediated distortion in metallic systems
G. Anand, Markus Eisenbach, Russell Goodall +1
The deviation of positions of atoms from their ideal lattice sites in crystalline solid state systems causes distortion and can lead to variation in structural [1] and functional p…
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…