Data-driven design of Nb-W refractory alloys using Transformer-based stress-strain modeling
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OpenAlex topics: Machine Learning in Materials Science · Model Reduction and Neural Networks · Additive Manufacturing Materials and Processes
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References (51)
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1. BCC-superalloys: perspectives and challenges10.1016/j.scriptamat.2025.116761 ↗
2. Developing novel low-density high-entropy superalloys with high strength and superior creep resistance guided by automated machine learning10.1016/j.actamat.2024.120656 ↗
3. Superior high-temperature strength in a supersaturated refractory high-entropy alloy10.1002/adma.202102401 ↗
4. Mechanism-aware digital twin for high-temperature creep prediction in Mo-re alloys
5. Development of a new thermo-mechanical processing route for Nb-5Mo-1Zr-0.1C (wt%) alloy10.1016/j.jallcom.2023.168860 ↗
6. Enhancing strength and ductility of Nb alloy via Y addition by vacuum electromagnetic levitation melting under oxygen carrier route10.1016/j.jmrt.2024.06.227 ↗
7. Ultra-strong tungsten refractory high-entropy alloy via stepwise controllable coherent nanoprecipitations10.1038/s41467-023-38531-4 ↗
8. Machine-learning design of ductile FeNiCoAlTa alloys with high strength10.1038/s41586-025-09160-2 ↗
9. Designing ductile refractory high-entropy alloys10.1038/s41578-024-00763-1 ↗
10. Structure-to-process modeling drives experimentally validated unified dual-phase steel10.1016/j.actamat.2025.121167 ↗
11. Multi-objective, multi-constraint high-throughput design, synthesis, and characterization of tungsten-containing refractory multi-principal element alloys10.1016/j.actamat.2024.120379 ↗
12. Enhanced strength and ductility in a high-entropy alloy via ordered oxygen complexes10.1038/s41586-018-0685-y ↗
13. A review on improving mechanical properties of high entropy alloy: interstitial atom doping10.1016/j.jmrt.2023.05.002 ↗
14. Phase transformation strengthening of high-temperature superalloys10.1038/ncomms13434 ↗
15. Dislocation flow turbulence simultaneously enhances strength and ductility10.1073/pnas.2316912121 ↗
16. Strength can be controlled by edge dislocations in refractory high-entropy alloys10.1038/s41467-021-25807-w ↗
17. Segregation-dislocation self-organized structures ductilize a work-hardened medium entropy alloy10.1038/s41467-025-56710-3 ↗
18. Thermal stabilizing and toughening of a dual-phase Nb alloy by tuning stabilizing element C in Nb-BCC10.1016/j.jmst.2023.09.060 ↗
19. Microstructure and mechanical properties of electron beam melted Nb–W and Nb–W–Zr alloys10.1007/s12666-023-03117-7 ↗
20. High-performance Nb alloy featuring a hierarchical carbides configuration for elevated-temperature applications10.1016/j.jmst.2024.08.039 ↗
21. Recent development of advanced materials for liquid rocket thruster chambers
22. Niobium—Base alloys10.1016/0263-4368(93)90038-H ↗
23. Stress-strain characterization of wire and arc additively manufactured aluminium alloys using the Ramberg-Osgood model
24. The relationship between stress and strain for homogeneous deformation
25. A review of hot deformation behavior and constitutive models to predict flow stress of high-entropy alloys10.1016/j.jallcom.2022.163964 ↗
26. Quasi-static and dynamic loading responses and constitutive modeling of titanium alloys10.1016/j.ijplas.2003.06.005 ↗
27. Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: theory, experiments, applications10.1016/j.actamat.2009.10.058 ↗
28. An analysis of nonuniform and localized deformation in ductile single crystals10.1016/0001-6160(82)90005-0 ↗
29. Crystal plasticity finite element analysis of gradient nanostructured TWIP steel10.1016/j.ijplas.2020.102703 ↗
30. Crystal plasticity finite element simulations on extruded Mg-10Gd rod with texture gradient10.1016/j.jma.2024.08.009 ↗
31. Probing the limits of metal plasticity with molecular dynamics simulations10.1038/nature23472 ↗
32. Extending the time scale in atomistic simulation of materials10.1146/annurev.matsci.32.112601.141541 ↗
33. Embedded-atom method: derivation and application to impurities, surfaces, and other defects in metals10.1103/PhysRevB.29.6443 ↗
34. Composition design of high-entropy alloys with deep sets learning10.1038/s41524-022-00779-7 ↗
35. Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility10.1016/j.jmrt.2025.04.037 ↗
36. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties10.1103/PhysRevLett.120.145301 ↗
37. Machine-learning-assisted determination of the global zero-temperature phase diagram of materials10.1002/adma.202210788 ↗
38. Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy10.1126/sciadv.abq5944 ↗
39. Data-driven design of mechanically hard soft magnetic high-entropy alloys10.1002/advs.202500867 ↗
40. Using neural networks to represent von Mises plasticity with isotropic hardening10.1016/j.ijplas.2020.102732 ↗
41. Robust deep learning framework for constitutive relations modeling10.1016/j.actamat.2023.118959 ↗
42. Transfer learning of recurrent neural network-based plasticity models10.1002/nme.7357 ↗
43. CPINet: parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM10.1016/j.euromechsol.2021.104327 ↗
44. Steel design based on a large language model10.1016/j.actamat.2024.120663 ↗
45. B. Zoph, I. Bello, S. Kumar, N. Du, Y. Huang, J. Dean, N. Shazeer, W. Fedus, St-moe: designing stable and transferable sparse expert models, arXiv preprint arXiv:2202.08906 (2022).
46. A. Liu, B. Feng, B. Wang, B. Wang, B. Liu, C. Zhao, C. Dengr, C. Ruan, D. Dai, D. Guo, Deepseek-v2: a strong, economical, and efficient mixture-of-experts language model, arXiv preprint arXiv:2405.04434 (2024).
47. D. Lepikhin, H. Lee, Y. Xu, D. Chen, O. Firat, Y. Huang, M. Krikun, N. Shazeer, Z. Chen, Gshard: scaling giant models with conditional computation and automatic sharding, arXiv preprint arXiv:2006.16668 (2020).
48. D. Dai, C. Deng, C. Zhao, R. Xu, H. Gao, D. Chen, J. Li, W. Zeng, X. Yu, Y. Wu, Deepseekmoe: towards ultimate expert specialization in mixture-of-experts language models, arXiv preprint arXiv:2401.06066 (2024).10.18653/v1/2024.acl-long.70 ↗
49. Time-series forecasting with deep learning: a survey10.1098/rsta.2020.0209 ↗
50. Tuning mechanical properties of ultrafine-grained tungsten by manipulating grain boundary chemistry10.1016/j.actamat.2022.117939 ↗
51. Synergistic effects of Hf and Ta on strength-ductility in refractory high-entropy alloys: kink band formation and dislocation evolution10.1016/j.actamat.2025.121492 ↗