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Xianglin Liu
Xianglin Liu
Amazon Web Services
Bestätigte E-Mail-Adresse bei andrew.cmu.edu
Titel
Zitiert von
Zitiert von
Jahr
Robust data-driven approach for predicting the configurational energy of high entropy alloys
J Zhang, X Liu, S Bi, J Yin, G Zhang, M Eisenbach
Materials & Design 185, 108247, 2020
352020
Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach
X Liu, J Zhang, J Yin, S Bi, M Eisenbach, Y Wang
Computational Materials Science 187, 110135, 2021
272021
Dislocation core structures and Peierls stresses of the high-entropy alloy NiCoFeCrMn and its subsystems
X Liu, Z Pei, M Eisenbach
Materials & Design 180, 107955, 2019
242019
Electronic transport and phonon properties of maximally disordered alloys: From binaries to high-entropy alloys
S Mu, Z Pei, X Liu, GM Stocks
Journal of Materials Research 33 (19), 2857-2880, 2018
242018
First-principles study of order-disorder transitions in multicomponent solid-solution alloys
M Eisenbach, Z Pei, X Liu
Journal of Physics: Condensed Matter 31 (27), 273002, 2019
112019
A full-potential approach to the relativistic single-site Green’s function
X Liu, Y Wang, M Eisenbach, GM Stocks
Journal of Physics: Condensed Matter 28 (35), 355501, 2016
112016
Machine learning modeling of high entropy alloy: the role of short-range order
X Liu, J Zhang, M Eisenbach, Y Wang
arXiv preprint arXiv:1906.02889, 2019
92019
LSMS
M Eisenbach, YW Li, X Liu, OD Odbadrakh, Z Pei, GM Stocks, J Yin
Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2017
82017
Fully-relativistic full-potential multiple scattering theory: A pathology-free scheme
X Liu, Y Wang, M Eisenbach, GM Stocks
Computer Physics Communications 224, 265-272, 2018
72018
Chemical complexity in high entropy alloys: a pair-interaction perspective
X Liu, J Zhang, S Bi, Y Wang, GM Stocks, M Eisenbach
arXiv preprint arXiv:1907.10223, 2019
42019
Machine learning for high-entropy alloys: Progress, challenges and opportunities
X Liu, J Zhang, Z Pei
Progress in Materials Science, 101018, 2022
2022
A data-driven approach to study the order-disorder transition in high entropy alloys
X Liu, J Zhang, J Yin, S Bi, M Eisenbach, Y Wang
APS March Meeting Abstracts 2021, V41. 014, 2021
2021
From LSMS to MuST: Large scale first principles materials calculations at the exascale
M Eisenbach, X Liu, M Karabin, S Ghosh, Y Wang, H Terletska, W Mondal, ...
APS March Meeting Abstracts 2021, S19. 002, 2021
2021
Machine Learning the Effective Hamiltonian in High Entropy Alloys with Large DFT Datasets
X Liu, J Zhang, Y Wang, M Eisenbach
Bulletin of the American Physical Society 65, 2020
2020
MuST: An integrated ab initio framework for the study of disordered structures
Y Wang, M Eisenbach, X Liu, K Odbadrakh, H Terletska, KM Tam, ...
Bulletin of the American Physical Society 65, 2020
2020
Machine Learning the Effective Hamiltonian in High Entropy Alloys
X Liu, J Zhang, M Eisenbach, Y Wang
arXiv preprint arXiv:1912.13460, 2019
2019
Application of full-potential LSMS method in high entropy alloys
X Liu, Y Wang, M Eisenbach, G Stocks
APS March Meeting Abstracts 2019, A22. 003, 2019
2019
Full-potential LSMS method for ab initio electronic structure calculations at large scale
Y Wang, X Liu, M Eisenbach, G Stocks
APS March Meeting Abstracts 2019, A22. 002, 2019
2019
A full-potential approach to the solution of core states
Z Liu, X Liu, Y Wang
APS March Meeting Abstracts 2018, L18. 009, 2018
2018
Full-potential fully relativistic LSMS Method
X Liu, M Eisenbach, Y Wang, GM Stocks
APS March Meeting Abstracts 2018, C34. 010, 2018
2018
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