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               3.  Ail, S. S.; Dasappa, S. Biomass to liquid transportation fuel via Fischer Tropsch synthesis – technology review and current scenario.
                  Renew. Sust. Energ. Rev. 2016, 58, 267-86. DOI
               4.  Schulz, H. Comparing Fischer-Tropsch synthesis on iron- and cobalt catalysts: the dynamics of structure and function. Stud. Surf. Sci.
                  Catal. 2007, 163, 177-99. DOI
               5.  Ma, W.; Jacobs, G.; Sparks, D. E.; Todic, B.; Bukur, D. B.; Davis, B. H. Quantitative comparison of iron and cobalt based catalysts for
                  the Fischer-Tropsch synthesis under clean and poisoning conditions. Catal. Today. 2020, 343, 125-36. DOI
               6.  Liu, Q. Y.; Shang, C.; Liu, Z. P. In situ active site for Fe-catalyzed Fischer–Tropsch synthesis: recent progress and future challenges. J.
                  Phys. Chem. Lett. 2022, 13, 3342-52. DOI
               7.  de Smit, E.; Cinquini, F.; Beale, A. M.; et al. Stability and reactivity of   -  -   iron carbide catalyst phases in Fischer-Tropsch synthesis:
                  controlling    C . J. Am. Chem. Soc. 2010, 132, 14928-41. DOI
               8.  Zhang, J.; Abbas, M.; Zhao, W.; Chen, J. Enhanced stability of a fused iron catalyst under realistic Fischer–Tropsch synthesis conditions:
                  insights into the role of iron phases (  -Fe 5C 2,   -Fe 3C and   -Fe). Catal. Sci. Technol. 2022, 12, 4217-27. DOI
               9.  Yang, C.; Zhao, H.; Hou, Y.; Ma, D. Fe 5C 2 nanoparticles: a facile bromide-induced synthesis and as an active phase for Fischer–Tropsch
                  synthesis. J. Am. Chem. Soc. 2012, 134, 15814-21. DOI
               10. Zhao, H.; Liu, J. X.; Yang, C.; et al. Synthesis of iron-carbide nanoparticles: identification of the active phase and mechanism of Fe-based
                  Fischer–Tropsch synthesis. CCS Chem. 2021, 3, 2712-24. DOI
               11. Cano, L. A.; Cagnoli, M. V.; Fellenz, N. A.; et al. Fischer-Tropsch synthesis. Influence of the crystal size of iron active species on the
                  activity and selectivity. Appl. Catal. A. Gen. 2010, 379, 105-10. DOI
               12. Torres Galvis, H. M.; Bitter, J. H.; Davidian, T.; Ruitenbeek, M.; Dugulan, A. I.; de Jong, K. P. Iron particle size effects for direct
                  production of lower olefins from synthesis gas. J. Am. Chem. Soc. 2012, 134, 16207-15. DOI
               13. Park, J. C.; Yeo, S. C.; Chun, D. H.; et al. Highly activated K-doped iron carbide nanocatalysts designed by computational simulation for
                  Fischer–Tropsch synthesis. J. Mater. Chem. A. 2014, 2, 14371-9. DOI
               14. Pham, T. H.; Qi, Y.; Yang, J.; et al. Insights into Hägg iron-carbide-catalyzed Fischer–Tropsch synthesis: suppression of CH 4 formation
                  and enhancement of C–C coupling on   -Fe 5C 2. ACS Catal. 2015, 5, 2203-8. DOI
               15. Song, N.; Cao, J.; Chen, B.; Qian, G.; Duan, X.; Zhou, X. CO adsorption and activation of   -Fe 2C Fischer–Tropsch catalyst. Ind. Eng.
                  Chem. Res. 2019, 58, 21296-303. DOI
               16. Chen, B.; Wang, D.; Duan, X.; et al. Charge-tuned CO activation over a   -Fe 5C 2 Fischer–Tropsch catalyst. ACS Catal. 2018, 8, 2709-14.
                  DOI
               17. Li, T.; Wen, X.; Yang, Y.; Li, Y. W.; Jiao, H. Mechanistic aspects of CO activation and C–C bond formation on the Fe/C- and Fe-terminated
                  Fe 3C(010) surfaces. ACS Catal. 2020, 10, 877-90. DOI
               18. Yin, J.; Liu, X.; Liu, X. W.; et al. Theoretical exploration of intrinsic facet-dependent CH 4 and C 2 formation on Fe 5C 2 particle. Appl.
                  Catal. B Environ. 2020, 278, 119308. DOI
               19. Zhang, X.; Wang, L.; Helwig, J.; et al. Artificial intelligence for science in quantum, atomistic, and continuum systems. arXiv 2023,
                  arXiv:2307.08423. Available online: https://arxiv.org/abs/2307.08423. (accessed on 17 Mar 2025)
               20. Ma, S.; Liu, Z. P. Machine learning for atomic simulation and activity prediction in heterogeneous catalysis: current status and future.
                  ACS Catal. 2020, 10, 13213-26. DOI
               21. Huang, S. D.; Shang, C.; Kang, P. L.; Zhang, X. J.; Liu, Z. P. LASP: fast global potential energy surface exploration. WIREs Comput.
                  Mol. Sci. 2019, 9, e1415. DOI
               22. Huang, S. D.; Shang, C.; Kang, P. L.; Liu, Z. P. Atomic structure of boron resolved using machine learning and global sampling. Chem.
                  Sci. 2018, 9, 8644-55. DOI
               23. Chen, D.; Chen, L.; Zhao, Q. C.; Yang, Z. X.; Shang, C.; Liu, Z. P. Square-pyramidal subsurface oxygen [Ag 4OAg] drives selective
                  ethene epoxidation on silver. Nat. Catal. 2024, 7, 536-45. DOI
               24. Liu, Q. Y.; Shang, C.; Liu, Z. P. In situ active site for CO activation in Fe-catalyzed Fischer-Tropsch synthesis from machine learning. J.
                  Am. Chem. Soc. 2021, 143, 11109-20. DOI
               25. Liu, Q. Y.; Chen, D.; Shang, C.; Liu, Z. P. An optimal Fe–C coordination ensemble for hydrocarbon chain growth: a full Fischer–Tropsch
                  synthesis mechanism from machine learning. Chem. Sci. 2023, 14, 9461-75. DOI
               26. van Steen, E.; Schulz, H. Polymerisation kinetics of the Fischer-Tropsch CO hydrogenation using iron and cobalt based catalysts. Appl.
                  Catal. A Gen. 1999, 186, 309-20. DOI
               27. Han, J.; Zhang, L.; Car, R.; Weinan, E. Deep potential: a general representation of a many-body potential energy surface. Commun.
                  Comput. Phys. 2018, 23, 629-39. DOI
               28. Wang, H.; Zhang, L.; Han, J.; Weinan, E. DeePMD-kit: a deep learning package for many-body potential energy representation and
                  molecular dynamics. Comput. Phys. Commun. 2018, 228, 178-84. DOI
               29. Zeng, J.; Zhang, D.; Lu, D.; et al. DeePMD-kit v2: a software package for deep potential models. J. Chem. Phys. 2023, 159, 054801. DOI
               30. DeePMD-kit. https://github.com/deepmodeling/deepmd-kit. (accessed on 2025-03-17)
               31. Zhang, L.; Han, J.; Wang, H.; Saidi, W. A.; Car, R.; Weinan, E. End-to-end symmetry preserving inter-atomic potential energy model
                  for finite and extended systems. arXiv 2018, arXiv:1805.09003. Available online: https://arxiv.org/abs/1805.09003. (accessed on 17 Mar
                  2025)
               32. Hedman, D.; McLean, B.; Bichara, C.; Maruyama, S.; Larsson, J. A.; Ding, F. Dynamics of growing carbon nanotube interfaces probed
                  by machine learning-enabled molecular simulations. Nat. Commun. 2024, 15, 4076. DOI
               33. Liu, J. C.; Luo, L.; Xiao, H.; Zhu, J.; He, Y.; Li, J. Metal affinity of support dictates sintering of gold catalysts. J. Am. Chem. Soc. 2022,
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