Breaking through the trade-off between saturation magnetization and coercivity: A data-driven strategy
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OpenAlex topics: Magnetic Properties and Applications · Magnetic properties of thin films · Machine Learning in Materials Science
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References (59)
Show source-provided references
1. Strategies for the synthesis of nanostructured SmCo5 magnetic particles for permanent magnetic application10.1021/acsanm.3c05776 ↗
2. Manipulate the magnetic properties of AlNiCo films by tuning non-magnetic phase structures10.1016/j.apsusc.2023.157584 ↗
3. Influence of severe plastic deformation on the magnetic properties of Sm–Co permanent magnets10.1002/adem.202400253 ↗
4. Recent developments in hard magnetic bulk materials
5. Heterostructures: new opportunities for functional materials10.1080/21663831.2019.1691668 ↗
6. Perspective and prospects for rare earth permanent magnets10.1016/j.eng.2018.11.034 ↗
7. Strong magnets with ordered structures10.1080/21663831.2021.2008541 ↗
8. Strong magnets by self-assembly10.1038/420374a ↗
9. Magnetic properties enhancement of multi-element-doped SmCo7 nanocrystalline alloys
10. How non-ferromagnetic Mn enhances the magnetization of SmCo7 based alloys10.1039/C9NR10483F ↗
11. Micromagnetics of rare-earth efficient permanent magnets10.1088/1361-6463/aab7d1 ↗
12. Structural stability and magnetic properties of SmCo5 compounds doped with transition metal elements10.1016/j.jallcom.2019.151888 ↗
13. Structural and magnetic properties of SmCo5−XNiX intermetallic compounds10.1016/j.jallcom.2021.160699 ↗
14. Predictions on the phase constitution of SmCo7−XMx alloys by data mining10.3390/nano12091452 ↗
15. Selecting doping elements by data mining for advanced magnets10.1021/acs.chemmater.9b03379 ↗
16. Predicting the Curie temperature of Sm-Co-based alloys via data-driven strategy10.1016/j.actamat.2024.120026 ↗
17. Machine learning accelerated, high throughput, multi-objective optimization of multiprincipal element alloys
18. Multi-objective materials bayesian optimization with active learning of design constraints: design of ductile refractory multi-principal-element alloys10.1016/j.actamat.2022.118133 ↗
19. Accelerated design for magnetic high entropy alloys using data-driven multi-objective optimization10.1039/D2TC03922B ↗
20. A multi-population multi-objective evolutionary algorithm based on the contribution of decision variables to objectives for large-scale multi/many-objective optimization10.1109/TCYB.2022.3180214 ↗
21. Muiltiobjective optimization using nondominated sorting in genetic algorithms10.1162/evco.1994.2.3.221 ↗
22. Structure and magnetic properties of SmCo7-xZrx alloys (x=0 to 0.8)
23. Phase transformation and magnetic properties of SmCo7−xBx alloys prepared by mechanical alloying10.1016/S0304-8853(01)00435-8 ↗
24. Magnetic properties of TbCu7-type SmCo7-xNix magnets produced by mechanical milling10.1109/TMAG.2008.2002997 ↗
25. Structure, phase transformation, and magnetic properties of SmCo7−xCrx magnets10.1063/1.2178397 ↗
26. Exchange coupling and magnetic behavior of SmCo5-xSnx alloys10.1016/S1005-0302(11)60052-2 ↗
27. A nanocrystalline Sm–Co compound for high-temperature permanent magnets10.1039/c3nr34134h ↗
28. Effects of Hf and CNTs on structure and magnetic properties of TbCu7-type Sm–Co magnets10.1016/j.intermet.2009.10.021 ↗
29. Structural and magnetic properties of RCo12-xTix (R = Y and Sm) and YFe12-xTix compounds10.1088/0022-3727/34/3/310 ↗
30. Nanocrystalline SmCo12 main-phase alloys with V-doping: structure stability and magnetic performance10.1016/j.jmst.2024.05.037 ↗
31. Ultrahard magnetism from mixed-valence dilanthanide complexes with metal-metal bonding10.1126/science.abl5470 ↗
32. Correlation of microchemistry of cell boundary phase and interface structure to the coercivity of Sm(Co0.784Fe0.100Cu0.088Zr0.028)7.19 sintered magnets10.1016/j.actamat.2016.12.050 ↗
33. Machine learning for materials research and development
34. Interpretable scientific discovery with symbolic regression: a review10.1007/s10462-023-10622-0 ↗
35. Heat-resistant aluminum alloy design using explainable machine learning10.1016/j.matdes.2024.113057 ↗
36. Phase classification of multi-principal element alloys via interpretable machine learning10.1038/s41524-022-00704-y ↗
37. SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
38. Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO10.1088/2515-7639/ab077b ↗
39. Distilling accurate descriptors from multi-source experimental data for discovering highly active perovskite OER catalysts10.1021/jacs.3c03493 ↗
40. Screening potential topological insulators in half-Heusler compounds via compressed-sensing
41. Improving symbolic regression for predicting materials properties with iterative variable selection10.1021/acs.jctc.2c00281 ↗
42. Superconductivity in antiperovskites10.1038/s41524-022-00817-4 ↗
43. SISSO-assisted prediction and design of mechanical properties of porous graphene with a uniform nanopore array10.1039/D1NA00457C ↗
44. Machine learning and structural design to optimize the flame retardancy of polymer nanocomposites with graphene oxide hydrogen bonded zinc hydroxystannate10.1021/acsami.1c12767 ↗
45. MEANDS: a many-objective evolutionary algorithm based on non-dominated decomposed sets applied to multicast routing10.1016/j.asoc.2017.09.017 ↗
46. Reducing the run-time complexity of multiobjective EAs: the NSGA-II and other algorithms10.1109/TEVC.2003.817234 ↗
47. Muiltiobjective optimization using nondominated sorting in genetic algorithms10.1162/evco.1994.2.3.221 ↗
48. Survey of multi-objective optimization methods for engineering10.1007/s00158-003-0368-6 ↗
49. A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems10.1109/ACCESS.2021.3070634 ↗
50. A co-evolutionary multi-objective optimization algorithm based on direction vectors10.1016/j.ins.2012.12.013 ↗
51. Quality evaluation of chicken soup based on entropy weight method and grey correlation degree method10.1038/s41598-024-61667-2 ↗
52. M. Winter, The University of Sheffield and WebElements Ltd, UK, WebElements, https://www.webelements.com.
53. Interpretation and prediction of the CO2 sequestration of steel slag by machine learning10.1021/acs.est.2c06133 ↗
54. A unified approach to interpreting model predictions
55. G.H. Rao, Effects of the doping element on crystal structure and magnetic properties of Sm(Co,M)(7) compounds (M = Si, Cu, Ti, Zr, and Hf)10.1016/j.intermet.2004.12.018 ↗
56. Effect of element doping on the structural stability, magnetic properties, and electronic structure of SmCo5-based rare earth permanent magnets10.1016/j.jmmm.2024.172492 ↗
57. Spark plasma sintered Sm2Co17–FeCo nanocomposite permanent magnets synthesized by high energy ball milling10.1088/0957-4484/19/33/335701 ↗
58. Nanocrystalline SmCo5 magnet synthesized by spark plasma sintering10.1063/1.3334458 ↗
59. Hard magnetic SmCo5-Cu nanocomposites produced by severe plastic deformation10.1016/j.actamat.2023.118709 ↗