Machine learning for high-entropy alloys: Progress, challenges and opportunities X Liu, J Zhang, Z Pei Progress in Materials Science 131, 101018, 2023 | 107 | 2023 |
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 | 62 | 2020 |
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 | 50 | 2021 |
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 | 37 | 2019 |
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 | 34 | 2018 |
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 | 16 | 2019 |
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 | 10 | 2016 |
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 | 9 | 2019 |
LSMS M Eisenbach, YW Li, X Liu, OD Odbadrakh, Z Pei, GM Stocks, J Yin Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2017 | 7 | 2017 |
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 | 6 | 2018 |
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 | 4 | 2019 |
Evaluation of atrial anatomical remodeling in atrial fibrillation with machine-learned morphological features F Zhou, Z Yuan, X Liu, K Yu, B Li, X Li, X Liu, G Cheng International Journal of Computer Assisted Radiology and Surgery 18 (4), 603-610, 2023 | 1 | 2023 |
Designing complex concentrated alloys with quantum machine learning and language modeling Z Pei, Y Gong, X Liu, J Yin Matter, 2024 | | 2024 |
A first principles investigation of electronic charge distribution in random alloys Y Wang, M Karabin, M Eisenbach, G Stocks, X Liu, W Mondal, H Terletska, ... APS March Meeting Abstracts 2022, F46. 009, 2022 | | 2022 |
MuST: A high performance ab initio framework for the study of disordered structures Y Wang, M Eisenbach, X Liu, M Karabin, S Ghosh, H Terletska, W Mondal, ... APS March Meeting Abstracts 2021, F22. 006, 2021 | | 2021 |
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 |