This group of work includes the method development and applications within quantum computing and quantum machine learning.
Representative publications:
5. (IF=45) Z. Pei*, J. Rogal, D. Sels, L. Zhang, and M. Tuckerman, Classical and quantum machine learning for materials physics, Review of Modern Physics (under review).
4 (IF=4.3) L. Wang, Y. Gong, Z. Pei*, Quantum and Hybrid Machine-Learning Models for Materials-Science Tasks, Advanced Quantum Technology, 9 (2), e00501, 2026
3. (IF=4.3) J. Del Castillo, D. Zhao, Z. Pei*, Comparative study of the ansätze in quantum language models, Advanced Quantum Technology 8 (10), e2500134, 2025.
2. (IF=18.9) Z. Pei*, Y. Gong, X. Liu and J. Yin, Designing complex concentrated alloys with quantum machine learning and text mining, Matter 7(2024)1-14.
1. (IF=7.8) Z. Pei*, Computer-aided drug discovery: From traditional simulation methods to language models and quantum computing, Cell Reports Physical Science 102334, 2024.
This group of work includes the method development and applications within AI/HPC.
Representative publications:
64. (IF=45) Z. Pei*, J. Rogal, D. Sels, L. Zhang, and M. Tuckerman, Classical and quantum machine learning for materials physics, Review of Modern Physics (under review).
61. (IF=37.4) Z. Pei*, J. Yin, J. Zhang, Language Models for Materials Discovery and Sustainability: Progress, Challenges, and Opportunities, Progress in Materials Science 154(2025)101495.
58. (IF=1.8) …, Z. Pei, X. Liu,…, Roadmap on Machine Learning-Based Interatomic Potentials, Modelling and Simulation in Materials Science and Engineering, 33 (2), 023301, 2025.
57. (IF=79.8) Z. Pei*, J. Yin, J. Neugebauer and A. Jain, Towards the holistic design of alloys with large language models, Nature Reviews Materials 2024.
56. (IF=7.8) Z. Pei*, Computer-aided drug discovery: From traditional simulation methods to language models and quantum computing, Cell Reports Physical Science 102334, 2024.
55. (IF=18.9) Z. Pei*, Y. Gong, X. Liu and J. Yin, Designing complex concentrated alloys with quantum machine learning and text mining, Matter 7(2024)1-14.
54. (IF=27.4) Y. Li, T. Colnaghi, Y. Gong, H. Zhang, Y. Yu, Y. Wei, B. Gan, M. Song, A. Marek, M. Rampp, S. Zhang, Z. Pei, M. Wuttig, S. Ghosh, F. Körmann, J. Neugebauer, Z. Wang, B. Gault, Machine Learning‐Enabled Tomographic Imaging of Chemical Short‐Range Atomic Ordering, Advanced Materials 2024, 2407564.
51. (IF=1.8) M. Scheffler, S Bauer, …, Z. Pei, …, Roadmap on Data-Centric Materials Science, Modelling and Simulation in Materials Science and Engineering, 32(2024)063301.
47. (IF=16.6) Z. Pei*, J. Yin, Peter K. Liaw, D. Raabe, Toward the design of ultrahigh-entropy alloys via mining six million texts, Nature Communications (2023)14:54.
45. (IF=37.4) X. Liu, J. Zhang, and Z. Pei* (Corresponding. Author), Machine learning for high entropy alloys: Progress, opportunities and challenges, Progress in Materials Science 131(2023)101018.
44. (IF=9.4) Y. Yan, Z. Pei, M.C. Gao, and K. Wang, Data-driven discovery of a formation prediction rule on high-entropy ceramics, Acta Materialia 253(2023)118955.
41. (IF=3.3) R. Li, M. Jin, Z. Pei, D. Wang, Geometrical defect detection on additive manufacturing parts with curvature feature and machine learning, The International Journal of Advanced Manufacturing Technology 120(2022)3719-3729.
36. (IF=15.1) Z. Pei*, K.A. Rozman, Ö.N. Doğan, Y. Wen, N. Gao, E.A. Holm, J.A. Hawk, D.E. Alman, M.C. Gao, Machine-learning microstructure for inverse material design, Advanced Science 8(2021)2101207.
35. (IF=12)J. Yin*, Z. Pei*, M.C. Gao, Neural network based order parameter for phase transitions and its applications in high-entropy alloys, Nature Computational Science 1(2021)686-693.
33. Z. Pei*, Mechanisms and Machine Learning for Magnesium Alloys Design, Magnesium Technology 2021 (book chapter).
31. (IF=9.7) Z. Pei*, J. Yin, J.A. Hawk, D.E. Alman, M.C. Gao, Machine-learning informed prediction of solid-solution formation: Beyond the Hume-Rothery rules, npj Computational Materials 6(2020)50.
30. (IF=9.7) Z. Pei*, R. Li, M.C. Gao, G.M. Stocks, Statistics of the NiCoCr medium-entropy alloy: Novel aspect of an old puzzle, npj Computational Materials 6(2020)122.
26. (IF=8.4) Z. Pei*, J. Yin, Machine Learning as a contributor to physics: Understanding Mg alloys, Materials & Design 153(2019)103-107.
This area includes all my computational work with different methods and materials. Methods across scales include density functional theory, atomistic simulations, Monte-Carlo methods, and multi-scale modeling. Materials includes lightweight alloys (Mg alloys, Al alloys, etc.), high-temperature materials (Ni-base superalloys, high-entropy ceramics, Martensitic steels, refractory metals and alloys, etc), complex concentrated alloys (both fcc and bcc), quantum materials (2D topological materials).
Representative publications:
64. (IF=45) Z. Pei*, J. Rogal, D. Sels, L. Zhang, and M. Tuckerman, Classical and quantum machine learning for materials physics, Review of Modern Physics (under review).
62. (IF=4.4) X. Liu, K. Yang, Y. Liu, F. Zhou, D. Fan, Z. Pei, P. Xu, Y. Tian, Revealing nanostructures in high-entropy alloys via machine-learning accelerated scalable Monte Carlo simulation, npj Computational Materials 11 (1), 267, 2025.
61. (IF=37.4) Z. Pei*, J. Yin, J. Zhang, Language Models for Materials Discovery and Sustainability: Progress, Challenges, and Opportunities, Progress in Materials Science 154(2025)101495.
60. Z. Pei*, X. Zhang, M. Eisenbach and P.K. Liaw, Rigorous computation of short-range order unifies its controversial effects in complex concentrated alloys, Acta Materialia 286(2025)120713.
58. (IF=1.8) …, Z. Pei, X. Liu,…, Roadmap on Machine Learning-Based Interatomic Potentials, Modelling and Simulation in Materials Science and Engineering, 33 (2), 023301, 2025.
56. (IF=7.8) Z. Pei*, Computer-aided drug discovery: From traditional simulation methods to language models and quantum computing, Cell Reports Physical Science 102334, 2024.
53. (IF=9.4) F. Zhang, Y. Ren, Z. Pei, Z. Lu, B. Wang, Y. Xue, X. Cao, K. Du, Y. Yang, Q. Gao, B. Li, M. Chen, Cooperative dislocations for pressure-dependent sequential deformation of a multi-principal element alloy, Acta Materialia 276(2024)120150.
52. (IF=9.8) P. Singh, W. Trehern, B. Vela, P. Sharma, T. Kirk, Z. Pei, R. Arroyave, M. C. Gao and D. D. Johnson, Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys, International Journal of Plasticity, 104020, 2024.
50. (IF=3.7) Z. Pei*, Synergistic Effect of Lattice Distortion and Random On-Site Energies in 2D Materials, Journal of Physical Chemistry C 2024, 128, 11, 4826-4831.
46. (IF=16.6) Z. Pei*, S. Zhao, M. Detrois, P.D. Jablonski, J.A. Hawk, D.E. Alman, M. Asta, A.M. Minor, M.C. Gao, Theory-guided design of high-entropy alloys with enhanced strength-ductility synergy, Nature Communications (2023)14:2519.
42. (IF=4.3) Z Liu, B Chen, P Zhao, L Yu, Z Pei, B Zhou, Y Hao, N Zhou, X Zeng, Atomic-scale characterization of the precipitates in a Mg-Gd-Y-Zn-Mn alloy using scanning transmission electron microscopy, Vacuum 207(2023)111668.
40. (IF=9.2) T Liu, Z Pei, D Barton, GB Thompson, L N Brewer, Characterization of nanostructures in a high pressure die cast Al-Si-Cu alloy, Acta Materialia 224(2022)117500.
39. (IF=11.1) Z. Pei*, S. Zhang, Y. Lei, F. Zhang, M. Chen, Decoupling between Shockley partial dislocations and stacking faults strengthens complex concentrated alloys, Proceedings of the National Academy of Sciences of the United States of America 118(2021)51.
38. (IF=16.6) R. Feng, C. Zhang, M.C. Gao, Z. Pei, F. Zhang, Y. Chen, D. Ma, K. An, J. Poplawsky, L. Ouyang, Y. Ren, J. Hawk, M. Widom, P.K. Liaw, High-throughput design of high-performance lightweight high-entropy alloys, Nature Communications 12(2021)4329.
37. (IF=8.6) Z. Pei*, B. Dutta, F. Koermann, M. Chen, Hidden effects of negative stacking fault energies in complex concentrated alloys, Physical Review Letters 126(2021)255502.
34. (IF=6.0) M. Detrois, Z. Pei, T. Liu, J.D. Poplawsky, M.C. Gao, P.D. Jablonski, J.A. Hawk, The detrimental effect of elemental contaminants when using B additions to improve the creep properties of a Ni-based superalloy, Scripta Materialia 201(2021)113971.
32. (IF=7.79) Z. Pei*, S. Mu, W. Ming, The local strain distribution in bilayer materials: A multiscale study, Nanoscale 12(2020)6456-6461.
29. (IF=8.4) Z. Pei*, J. Yin, The relation between two ductility mechanisms revealed by high throughput simulations, Materials & Design 186(2020)108286.
28. M. Detrois, Z. Pei, KA Rozman, MC Gao, JD Poplawsky, PD Jablonski, JA Hawk, Partitioning of tramp elements Cu and Si in a Ni-based superalloy and their effect on creep properties, Materialia (2020)100843.
27. (IF=2.5) Z. Liu, Z. Pei, B. Chen, XQ Zeng, Atomic-scale insights on the plate-shaped phase in Mg–Gd–Y–Ag–Zr alloy, Journal of Materials Research 35(2020)1837.
25. (IF=4.3) Z. Liu, Z. Pei, B. Chen, W. Ding, X. Zeng, Unexpected capture of Guinier-Preston zone and γ" phase in as-cast Mg-Gd-Y-Zn-Ni-Mn alloy: Atomic-scale insights, Materials Characterization 172(2019)107795.
24. (IF=2.3) M. Eisenbach*, Z. Pei*, X. Liu*, First-principles study of order-disorder transitions in multicomponent solid-solution alloys, J. Physics: Condensed Matter. 31(2019)27.
23. (IF=9.4) W. Guo, Z. Pei, H. Bei, S. Bruschi, J.D. Poplawsky, D. Raabe, Shape-preserving machining produces gradient nanolaminate medium entropy alloys with high energy absorption capability, Acta Materialia 170(2019)176-186.
22. (IF=3.4) S. Mu, J. Yin, G. M. Samolyuk, S. Wimmer, Z. Pei, M. Eisenbach, S. Mankovsky, H. Ebert, G.M. Stocks, Hidden Mn magnetic-moment disorder and its influence on the physical properties of medium-entropy NiCoMn solid solution alloys, Physical Review Materials 3(2019) 014411.
21. (IF=8.4) Z. Pei*, R.Li, J.F. Nie and J. R. Morris, First-principles study of the solute segregation in twin boundaries in Mg and possible descriptors for mechanical properties, Materials & Design 165(2019)107574.
20. (IF=6.0, S) Z. Pei, Theory of the energy fluctuation of multicomponent alloys, Scripta Materialia 162(2019)503-506.
19. (IF=8.4) X. Liu, Z. Pei* and M. Eisenbach, Dislocation Core Structures and Peierls Stresses of NiCoFeCrMn High-Entropy Alloy and Its Subsystems, Materials & Design 180(2019)107955.
18. (IF=6.3) Z. Pei*, M. Eisenbach, S. Mu, G.M. Stocks, Error controlling of the combined Cluster-Expansion and Wang-Landau Monte-Carlo method and its application to FeCo, Computer Physics Communications 235(2018)95-101.
17. (IF=9.4) Q. Xie#, Z. Pei#, J. Liang#, D. Yu, Z. Zhao, P. Yang, R. Li, M. Eisenbach, A. Ke, In-situ neutron diffraction and theoretical study on the micromechanics of tensile loading, unloading and reloading for the TWIP steel after 50% fatigue life, Acta Materialia 161(2018)273-284. (# Equal contribution)
16. (IF=6.4, S) Z. Pei*, An overview of modeling the stacking faults in lightweight and high-entropy alloys: theory and application, Materials Science & Engineering A 737(2018)132-150.
15. (IF=2.50) S. Mu, Z. Pei, X. Liu and G. M. Stocks, Electronic transport and phonon properties of maximally disordered alloys: from binaries to high entropy alloys, Journal of Materials Research 33(2018)2857-2880.
14. (IF=8.4) Z. Pei*, H. Sheng, X. Zhang, R. Li, B. Svendsen, Tunable twin stability and an accurate magnesium interatomic potential for dislocation-twin interactions, Materials & Design 153(2018)232-241.
11. (IF=4.6) S. Sandlöbes, M. Friák, S. Korte-Kerzel, Z. Pei, J. Neugebauer, D. Raabe, Rare-earth free intrinsically ductile Magnesium, Scientific Reports 7(2017)10458.
10. (IF=9.7) Z. Pei*, X. Zhang, T. Hickel, M. Friák, S. Sandlöbes, B. Dutta, J. Neugebauer, Atomic structures of twin boundaries in hexagonal close- packed metallic crystals with particular focus on Mg, npj Computational Materials (2017) 3:6
9. Z. Pei, D. Chu and S. Mu, Report of the big data challenge: Automated Discovery of Temperature Dependent Structural Change, Smoky Mountain Computational Sciences and Engineering Conference (SMC).
8. (IF=6.3) Z. Pei*, M. Eisenbach, Acceleration of the Particle Swarm Optimization for Peierls–Nabarro modeling of dislocations in conventional and high-entropy alloys, Computer Physics Communications 215,7-12.
7. (IF=3.6) Z. Pei*, R. Li, The effect of yttrium on the generalized stacking fault energies in Mg, Computational Materials Science 133(2018)1-5.
6. Z. Pei, A theory-guided combinatorial materials design of ductile Mg-based alloys utilizing ab initio and atomistic methods, Dissertation, RWTH-Aachen University (REPORT NUMBER: RWTH-2015-04333).
5. (IF=3.7) Z. Pei*, D. Ma, M. Friák, B. Svendsen, D. Raabe, J. Neugebauer, From generalized stacking fault energies to dislocation cores: impact of solutes on the Peierls stress in magnesium, Physical Review B 92(2015)064107.
4. (IF=3.3) Z. Pei, M. Friák, S. Sandlöbes, R. Nazarov, B. Svendsen, D. Raabe, J. Neugebauer, Rapid theory-guided prototyping of ductile Mg alloys: from binary to higher-order materials, New Journal of Physics 17(2015)093009.
3. (IF=9.4) S. Sandlöbes, Z. Pei, M. Friák, L.-F. Zhu, F. Wang, S. Zaefferer, D. Raabe, J. Neugebauer, Ductility improvement of Mg alloys by solid solution: Ab initio modeling, synthesis and mechanical properties, Acta Materialia 70(2014)92-104.
2. (IF=3.3) Z. Pei, L.-F. Zhu, M. Friák,S. Sandlöbes, J. von Pezold, H. W. Sheng, C. P. Race, S. Zaefferer, B. Svendsen, D. Raabe, J. Neugebauer, Ab initio and atomistic study of generalized stacking fault energies in Mg and Mg-Y alloys, New Journal of Physics 15 (4), 043020, 2013.
1. (IF=9.4) S. Sandlöbes, M. Friák, S. Zaefferer, A. Dick, S. Yi, D. Letzig, Z. Pei, L.-F. Zhu, J. Neugebauer, D. Raabe,The relation between ductility and stacking fault energies in Mg and Mg-Y alloys, Acta Materialia 60 (6), 3011-3021, 2012.
This group of work includes the method development and applications within the mechanics of materials.
Representative publications:
59. (IF=7.0) Z. Pei*, Diminishing Marginal Effect and Law of Mass Action in Binary Concentrated Alloys, Materials Science and Engineering A 942(2025)148647.
49. (IF=6.2) Z. Pei*, Connecting the mechanical properties of magnesium and high-entropy alloys, Journal of Alloys and Compounds 966(2023)171462.
48. (IF=3.6) Z. Pei*, A general framework for dislocation models, Computational Materials Science 222(2023)112107.
46. (IF=16.6) Z. Pei*, S. Zhao, M. Detrois, P.D. Jablonski, J.A. Hawk, D.E. Alman, M. Asta, A.M. Minor, M.C. Gao, Theory-guided design of high-entropy alloys with enhanced strength-ductility synergy, Nature Communications (2023)14:2519.
43. (IF=10.3) Z. Pei*, M. Eisenbach, P. K. Liaw, M. Chen, Nanoscale fluctuation of stacking fault energy strengthens multi-principal element alloys, Journal of Materials Science and Technology 158(2023)218-225.
37. (IF=8.6) Z. Pei*, B. Dutta, F. Koermann, M. Chen, Hidden effects of negative stacking fault energies in complex concentrated alloys, Physical Review Letters 126(2021)255502.
32. (IF=7.79) Z. Pei*, S. Mu, W. Ming, The local strain distribution in bilayer materials: A multiscale study, Nanoscale 12(2020)6456-6461.
19. (IF=8.4) X. Liu, Z. Pei* and M. Eisenbach, Dislocation Core Structures and Peierls Stresses of NiCoFeCrMn High-Entropy Alloy and Its Subsystems, Materials & Design 180(2019)107955.
13. (IF=6.3, S) Z. Pei*, DIST: A Dislocation-Simulation Toolkit, Computer Physics Communications 233(2018)44-50.
12. (IF=9.8) Z. Pei*, G. M. Stocks, Origin of the sensitivity in modeling the glide behaviour of dislocations, International Journal of Plasticity 106(2018)45-56.
8. (IF=6.3) Z. Pei*, M. Eisenbach, Acceleration of the Particle Swarm Optimization for Peierls–Nabarro modeling of dislocations in conventional and high-entropy alloys, Computer Physics Communications 215,7-12.
5. (IF=3.7) Z. Pei*, D. Ma, M. Friák, B. Svendsen, D. Raabe, J. Neugebauer, From generalized stacking fault energies to dislocation cores: impact of solutes on the Peierls stress in magnesium, Physical Review B 92(2015)064107.
Zongrui Pei, Riemann zeros and the KKR determinant, Proceedings of the Royal Society A Mathematical Physical and Engineering Science, 482, 2333, 2026 (arXiv preprint arXiv:2504.07928, 2025).