Machine Learning Prediction of Density for Binary Mg-Containing Phases
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Machine Learning in Materials Science · X-ray Diffraction in Crystallography · Thermodynamic and Structural Properties of Metals and Alloys
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (50)
Show source-provided references
1. Magnesium is at a crossroads: an industrial metal or a technology metal10.1016/j.jma.2024.12.017 ↗
2. Magnesium research and applications: past, present and future10.1016/j.jma.2023.11.007 ↗
3. High-strength extruded magnesium alloys: a critical review10.1016/j.jmst.2024.01.089 ↗
4. Applications of magnesium alloys for aerospace: a review10.1016/j.jma.2023.09.015 ↗
5. Applications of magnesium and its alloys: a review10.3390/app11156861 ↗
6. Towards developing Mg alloys with simultaneously improved strength and corrosion resistance via RE alloying10.1016/j.jma.2020.08.016 ↗
7. Machine learning approaches for predicting and validating mechanical properties of Mg rare earth alloys for light weight applications10.1080/14686996.2025.2449811 ↗
8. Additive manufacturing high-strength and ultra-high-rare-earth magnesium alloys: excellent long-time aging hardening and strengthening behavior
9. A critical review on the comparative assessment of rare-earth and non-rare-earth alloying in magnesium alloys10.3390/met15020128 ↗
10. Mechanisms of temperature effects on the tensile properties and dislocation behavior of Mg-Zn-Ca alloys10.1038/s41598-025-01805-6 ↗
11. Wu · Corros Sci · 2024 · 235 · 11216610.1016/j.corsci.2024.112166 ↗
12. Enhancement of strength and elastic modulus of Mg-Gd-Y-Zn-Zr alloy by Sn addition10.1016/j.msea.2022.143885 ↗
13. An ultra-light Mg-Li alloy with exceptional elastic modulus, high strength, and corrosion-resistance10.1016/j.mtcomm.2023.105623 ↗
14. Mao · J Magnes Alloys · 2013 · 1 · 25610.1016/j.jma.2013.10.001 ↗
15. Machine-learning interatomic potentials for materials science10.1016/j.actamat.2021.116980 ↗
16. Machine learning for alloys10.1038/s41578-021-00340-w ↗
17. Machine learning in materials genome initiative: a review10.1016/j.jmst.2020.01.067 ↗
18. Predicting the magnetic behaviour of homogenized CoCrFeNiAlx high entropy alloys at different aluminium content and temperatures: reducing experimental dependency through machine learning approaches10.1016/j.matchemphys.2025.131386 ↗
19. Machine learning-driven prediction of microstructure-mechanical property relationships in Mg-Al alloys10.1016/j.jallcom.2025.181995 ↗
20. Machine learning accelerated design of magnesium alloys with high strength and high ductility10.1016/j.mtcomm.2025.111894 ↗
21. Modeling the correlation between texture characteristics and tensile properties of AZ31 magnesium alloy based on the artificial neural networks10.1016/j.jmrt.2023.04.079 ↗
22. Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning10.1016/j.matlet.2023.134605 ↗
23. A machine learning enabled ultra-fine grain design strategy of Mg-Mn-based alloys10.1016/j.jmrt.2023.02.091 ↗
24. A brief review of machine learning-assisted Mg alloy design, processing, and property predictions10.1016/j.jmrt.2024.05.139 ↗
25. Machine learning dislocation density correlations and solute effects in Mg-based alloys10.1038/s41598-023-37633-9 ↗
26. Predicting mechanical properties of magnesium matrix composites with regression models by machine learning10.3390/jcs7090347 ↗
27. Machine learning-based prediction of phases in high-entropy alloys: a data article10.1016/j.dib.2021.107346 ↗
28. Machine learning guided BCC or FCC phase prediction in high entropy alloys10.1016/j.jmrt.2024.01.257 ↗
29. Implementation of an intelligent power management system for building using machine learning model
30. Scikit-learn: machine learning in python
31. A strategy to apply machine learning to small datasets in materials science10.1038/s41524-018-0081-z ↗
32. Prediction of electronic work function of the second phase in binary magnesium alloy based on machine learning method10.1557/s43578-022-00752-6 ↗
33. The proof and measurement of association between two things10.2307/1412159 ↗
34. Machine learning-based prediction models for formation energies of interstitial atoms in HCP crystals10.1016/j.scriptamat.2020.02.042 ↗
35. Commentary: the materials project: a materials genome approach to accelerating materials innovation10.1063/1.4812323 ↗
36. Accelerated data-driven materials science with the materials project
37. Materials Project [Internet]. [cited 2025 Sep 1]. Available from: http://next-gen.materialsproject.org.
38. The work function of the elements and its periodicity10.1063/1.323539 ↗
39. Coupling physics in machine learning to investigate the solution behavior of binary Mg alloys10.1016/j.jma.2021.06.014 ↗
40. The Photographic Periodic Table of the Elements [Internet]. [cited 2025 Sep 1]. Available from: http://periodictable.com.
41. Quantitative analysis and identification of magnesium alloys using fs-LA-SIBS combined with machine learning methods10.1039/D4RA07007K ↗
42. Random forest classifier for high entropy alloys phase diagnosis10.1007/s13370-024-01198-1 ↗
43. Prediction of electrochemical corrosion behavior of magnesium alloy using machine learning methods10.1016/j.mtcomm.2023.107285 ↗
44. Random forests10.1023/A:1010933404324 ↗
45. Support-vector networks10.1023/A:1022627411411 ↗
46. Bučinskas A. Investigation of structure-properties relationship of organic carbazolyl-containing semiconductors [master’s thesis]. Kaunas, Lietuva: Kauno Technologijos Universitetas; 2016.
47. Lieb · The stability of matter: from atoms to stars · 1997 · 78
48. Effect of impurity elements on the corrosion behavior of Mg-Al alloys: a first principles study10.1016/j.jmrt.2025.05.078 ↗
49. Interaction of elements in dilute Mg alloys: a DFT and machine learning study10.1016/j.jmrt.2022.11.071 ↗
50. A unified approach to interpreting model predictions