REFERENCES
1. Ge, W.; De, Silva. R.; Fan, Y.; Sisson, S. A.; Stenzel, M. H. Machine learning in polymer research. Adv. Mater. 2025, 37, 2413695.
2. Bartók, A. P.; Kermode, J.; Bernstein, N.; Csányi, G. Machine learning a general-purpose interatomic potential for silicon. Phys. Rev. X. 2018, 8, 041048.
3. Rohskopf, A.; Seyf, H. R.; Gordiz, K.; Tadano, T.; Henry, A. Empirical interatomic potentials optimized for phonon properties. Npj. Comput. Mater. 2017, 3, 27.
4. Finnis, M. W.; Sinclair, J. E. A simple empirical N-body potential for transition metals. Philos. Mag. A. 1984, 50, 45-55.
5. Daw, M. S.; Baskes, M. I. Embedded-atom method: derivation and application to impurities, surfaces, and other defects in metals. Phys. Rev. B. 1984, 29, 6443.
6. Baskes, M. I. Modified embedded-atom potentials for cubic materials and impurities. Phys. Rev. B. 1992, 46, 2727.
7. Fedik, N.; Zubatyuk, R.; Kulichenko, M.; et al. Extending machine learning beyond interatomic potentials for predicting molecular properties. Nat. Rev. Chem. 2022, 6, 653-72.
8. Mortazavi, B.; Zhuang, X.; Rabczuk, T.; Shapeev, A. V. Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials. Mater. Horiz. 2023, 10, 1956-68.
9. Kulichenko, M.; Nebgen, B.; Lubbers, N.; et al. Data generation for machine learning interatomic potentials and beyond. Chem. Rev. 2024, 124, 13681-714.
10. Chen, C.; Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2022, 2, 718-28.
11. Ito, K.; Yokoi, T.; Hyodo, K.; Mori, H. Machine learning interatomic potential with DFT accuracy for general grain boundaries in α-Fe. Npj. Comput. Mater. 2024, 10, 255.
12. Ji, Y.; Liang, J.; Xu, Z. Machine-learning interatomic potentials for long-range systems. Phys. Rev. Lett. 2025, 135, 178001.
13. Wan, K.; He, J.; Shi, X. Construction of high accuracy machine learning interatomic potential for surface/interface of nanomaterials - a review. Adv. Mater. 2024, 36, 2305758.
14. Jordan, M. I.; Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 2015, 349, 255-60.
15. Hinton, G. E.; Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 2006, 313, 504-7.
16. Sharifani, K.; Amini, M. Machine learning and deep learning: a review of methods and applications. World. Inf. Technol. Eng. J. 2023, 10, 3897-904. https://ssrn.com/abstract=4458723. (accessed on 23 Jul 2026).
17. Carleo, G.; Cirac, I.; Cranmer, K.; et al. Machine learning and the physical sciences. Rev. Mod. Phys. 2019, 91, 045002.
18. Wang, Q.; Yao, Y. Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction. Npj. Comput. Mater. 2025, 11, 91.
19. Liu, X.; Zhang, J.; Pei, Z. Machine learning for high-entropy alloys: progress, challenges and opportunities. Prog. Mater. Sci. 2023, 131, 101018.
20. Zhang, Y.; Wang, H.; Chen, W.; et al. DP-GEN: a concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput. Phys. Commun. 2020, 253, 107206.
21. Galib, M.; Isiet, M.; Ponga, M. AtomProNet: data flow to and from machine learning interatomic potentials in materials science. arXiv 2025, arXiv:2501.14039. Available online: https://doi.org/10.48550/arXiv.2501.14039. (accessed on 23 Jul 2026).
22. Li, W.; Ou, Q.; Chen, Y.; et al. DeePKS+ ABACUS as a bridge between expensive quantum mechanical models and machine learning potentials. J. Phys. Chem. A. 2022, 126, 9154-64.
23. Chen, C.; Li, Y.; Zhao, R.; et al. NepTrain and NepTrainKit: automated active learning and visualization toolkit for neuroevo-lution potentials. Comput. Phys. Commun. 2025, 317, 109859.
24. Cao, Y.; Sheriff, K.; Freitas, R. Capturing short-range order in high-entropy alloys with machine learning potentials. npj. Comput. Mater. 2025, 11, 268.
25. Röcken, S.; Zavadlav, J. Accurate machine learning force fields via experimental and simulation data fusion. npj. Comput. Mater. 2024, 10, 69.
26. Sauceda, H. E.; Gálvez-González, L. E.; Chmiela, S.; Paz-Borbón, L. O.; Müller, K. R.; Tkatchenko, A. BIGDML - towards accurate quantum machine learning force fields for materials. Nat. Commun. 2022, 13, 3733.
27. Shapeev, A. V. Moment tensor potentials: a class of systematically improvable interatomic potentials. Multiscale. Model. Simul. 2016, 14, 1153-73.
28. Liu, J.; Byggmästar, J.; Fan, Z.; Qian, P.; Su, Y. Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten. Phys. Rev. B. 2023, 108, 054312.
29. Kang, S.; Kim, J.; Park, T.; et al. Toward fast and accurate machine learning interatomic potentials for atomic layer deposition precursors. Mater. Today. Adv. 2024, 21, 100474.
30. Mortazavi, B.; Novikov, I. S.; Podryabinkin, E. V.; et al. Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials. Appl. Mater. Today. 2020, 20, 100685.
31. Rose, J. H.; Smith, J. R.; Guinea, F.; Ferrante, J. Universal features of the equation of state of metals. Phys. Rev. B. 1984, 29, 2963.
32. Hixson, R. S.; Fritz, J. N. Shock compression of tungsten and molybdenum. J. Appl. Phys. 1992, 71, 1721-8.
33. Ma, P. W.; Dudarev, S. L. Universality of point defect structure in body-centered cubic metals. Phys. Rev. Mater. 2019, 3, 013605.
34. Ma, P. W.; Dudarev, S. L. Effect of stress on vacancy formation and migration in body-centered-cubic metals. Phys. Rev. Mater. 2019, 3, 063601.
35. Mason, D. R.; Nguyen-Manh, D.; Becquart, C. S. An empirical potential for simulating vacancy clusters in tungsten. J. Phys. Condens. Matter. 2017, 29, 505501.
36. Alexander, R.; Marinica, M. C.; Proville, L.; et al. Ab initio scaling laws for the formation energy of nanosized interstitial defect clusters in iron, tungsten, and vanadium. Phys. Rev. B. 2016, 94, 024103.
37. Bonny, G.; Terentyev, D.; Bakaev, A.; Grigorev, P.; Van Neck, D. Many-body central force potentials for tungsten. Model. Simul. Mater. Sci. Eng. 2014, 22, 053001.
38. Gehringer, D.; Friák, M.; Holec, D. Models of configurationally-complex alloys made simple. Comput. Phys. Commun. 2023, 286, 108664.
39. Gilbert, M. R.; Sublet, J. C. Neutron-induced transmutation effects in W and W-alloys in a fusion environment. Nucl. Fusion. 2011, 51, 043005.
40. Romaner, L.; Ambrosch-Draxl, C.; Pippan, R. Effect of rhenium on the dislocation core structure in tungsten. Phys. Rev. Lett. 2010, 104, 195503.
41. Tanno, T.; Fukuda, M.; Nogami, S.; Hasegawa, A. Microstructure development in neutron irradiated tungsten alloys. Mater. Trans. 2011, 52, 1447-51.
42. Li, Y. H.; Zhou, H. B.; Liang, L.; et al. Transition from ductilizing to hardening in tungsten: the dependence on rhenium distribution. Acta. Mater. 2019, 181, 110-23.
43. Szlachta, W. J.; Bartók, A. P.; Csányi, G. Accuracy and transferability of Gaussian approximation potential models for tungsten. Phys. Rev. B. 2014, 90, 104108.
44. Muzyk, M.; Nguyen-Manh, D.; Kurzydłowski, K. J.; Baluc, N. L.; Dudarev, S. L. Phase stability, point defects, and elastic properties of WV and W-Ta alloys. Phys. Rev. B. 2011, 84, 104115.
45. Bonny, G.; Bakaev, A.; Terentyev, D.; Mastrikov, Y. A. Interatomic potential to study plastic deformation in tungsten-rhenium alloys. J. Appl. Phys. 2017, 121, 165107.
46. Mrovec, M.; Gröger, R.; Bailey, A. G.; Nguyen-Manh, D.; Elsässer, C.; Vitek, V. Bond-order potential for simulations of extended defects in tungsten. Phys. Rev. B. 2007, 75, 104119.
47. Featherston, F. H.; Neighbours, J. R. Elastic constants of tantalum, tungsten, and molybdenum. Phys. Rev. 1963, 130, 1324.
48. Wang, Y.; Chen, D.; Zhang, X. Calculated equation of state of Al, Cu, Ta, Mo, and W to 1000 GPa. Phys. Rev. Lett. 2000, 84, 3220.
49. Ferroni, F.; Yi, X.; Arakawa, K.; Fitzgerald, S. P.; Edmondson, P. D.; Roberts, S. G. High temperature annealing of ion irradiated tungsten. Acta. Mater. 2015, 90, 380-93.
50. Chen, Y.; Li, Y. H.; Gao, N.; et al. New interatomic potentials of W, Re and W-Re alloy for radiation defects. J. Nucl. Mater. 2018, 502, 141-53.
51. Wang, X.; Wang, Y.; Zhang, L.; Dai, F.; Wang, H. A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment. Nucl. Fusion. 2022, 62, 126013.
52. Byggmästar, J.; Hamedani, A.; Nordlund, K.; Djurabekova, F. Machine-learning interatomic potential for radiation damage and defects in tungsten. Phys. Rev. B. 2019, 100, 144105.
53. Lide, D. R. CRC handbook of chemistry and physics: a ready-reference book of chemical and physical data. CRC Press; 1995. https://books.google.com/books/about/CRC_Handbook_of_Chemistry_and_Physics.html?id=q2qJId5TKOkC&utm_source=chatgpt.com. (accessed on 23 Jul 2026).
54. Qi, X.; Cai, N.; Wang, S.; Li, B. Thermoelastic properties of tungsten at simultaneous high pressure and temperature. J. Appl. Phys. 2020, 128, 105105.
55. Renault, P. O.; Badawi, K. F.; Bimbault, L.; Goudeau, P.; Elkaım, E.; Lauriat, J. P. Poisson’s ratio measurement in tungsten thin films combining an x-ray diffractometer with in situ tensile tester. Appl. Phys. Lett. 1998, 73, 1952-4.
56. Kittel, P. Enthalpy, entropy, and exergy flow losses in pulse tube cryocoolers. In Cryocoolers 13. Springer; 2005. pp. 343-52.
57. Simmons, G. Single crystal elastic constants and calculated aggregate properties. Cambridge, MA: MIT Press; 1971. https://scholar.smu.edu/cgi/viewcontent.cgi?article=1029&context=journal_grc. (accessed on 23 Jul 2026).
58. You, Y.; Zhang, D.; Wu, F.; et al. Principal component analysis enables the design of deep learning potential precisely capturing LLZO phase transitions. npj. Comput. Mater. 2024, 10, 57.
59. He, S.; Mang, E. H.; El Atwani, O.; et al. Complex dislocation loop networks as natural extensions of the sink efficiency of saturated grain boundaries in irradiated metals. Sci. Adv. 2024, 10, eadj8395.
60. Borges, P. P. P. O.; Ritchie, R. O.; Asta, M. Electronic descriptors for dislocation deformation behavior and intrinsic ductility in bcc high-entropy alloys. Sci. Adv. 2024, 10, eadp7670.
61. Hou, J.; Peng, D.; Kong, X. S.; et al. Hydrogen modulated dislocation reaction and defect accumulation in bcc metals. Acta. Mater. , 2025, 121524.
62. Parakh, A.; Lee, S.; Harkins, K. A.; et al. Nucleation of dislocations in 3.9 nm nanocrystals at high pressure. Phys. Rev. Lett. 2020, 124, 106104.
63. Li, G.; Wen, H.; Zhang, Y.; et al. Electromechanical coupled modulation of dislocation nucleation and annihilation in ferroelectric oxide films. Phys. Rev. B. 2025, 111, 224101.
64. Kanhaiya, K.; Kim, S.; Im, W.; Heinz, H. Accurate simulation of surfaces and interfaces of ten FCC metals and steel using Lennard–Jones potentials. npj. Comput. Mater. 2021, 7, 17.
65. Zhu, L. F.; Srinivasan, P.; Gong, Y.; et al. Melting properties of the refractory metals V and W and the binary VW alloy fully from first principles. Phys. Rev. B. 2024, 109, 094110.
66. Kresse, G.; Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B. 1994, 49, 14251.
67. Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B. 1996, 54, 11169.
