Machine learning based approach for phase prediction in high entropy borides
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OpenAlex topics: High Entropy Alloys Studies · Advanced materials and composites · Metal and Thin Film Mechanics
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References (63)
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1. High-entropy ceramics: present status, challenges, and a look forward10.1007/s40145-021-0477-y ↗
2. High-entropy high-hardness metal carbides discovered by entropy descriptors10.1038/s41467-018-07160-7 ↗
3. Entropy-stabilized oxides10.1038/ncomms9485 ↗
4. High-entropy metal diborides: a new class of high-entropy materials and a new type of ultrahigh temperature ceramics10.1038/srep37946 ↗
5. Dense high-entropy boride ceramics with ultra-high hardness10.1016/j.scriptamat.2019.01.021 ↗
6. Novel processing route for the fabrication of bulk high-entropy metal diborides10.1016/j.scriptamat.2018.08.039 ↗
7. High-entropy alloy: challenges and prospects10.1016/j.mattod.2015.11.026 ↗
8. High-entropy alloys and metallic nanocomposites: processing challenges, microstructure development and property enhancement
9. Lattice distortion in high‐entropy carbide ceramics from first‐principles calculations10.1111/jace.17600 ↗
10. High-entropy materials: critical review and way forward10.18520/cs/v118/i10/1520-1539 ↗
11. Microstructures and properties of high-entropy alloys10.1016/j.pmatsci.2013.10.001 ↗
12. A novel, single phase, non-equiatomic FeMnNiCoCr high-entropy alloy with exceptional phase stability and tensile ductility10.1016/j.scriptamat.2013.09.030 ↗
13. On the superior hot hardness and softening resistance of AlCoCrxFeMo0.5Ni high-entropy alloys10.1016/j.msea.2011.01.072 ↗
14. Discovery of high-entropy ceramics via machine learning
15. C. Zhang, M.C. Gao, CALPHAD Modeling of High-Entropy Alloys, High-Entropy Alloys, Springer2016, pp. 399-444.10.1007/978-3-319-27013-5_12 ↗
16. In the quest of single phase multi-component multiprincipal high entropy alloys10.1016/j.jallcom.2016.11.383 ↗
17. Phase equilibria in equiatomic CoCuFeMnNi high entropy alloy10.1016/j.matchemphys.2017.08.051 ↗
18. Hume-Rothery rules for structurally complex alloy phases10.1557/mrs.2012.45 ↗
19. Guidelines in predicting phase formation of high-entropy alloys10.1557/mrc.2014.11 ↗
20. Phase stability in high entropy alloys: formation of solid-solution phase or amorphous phase10.1016/S1002-0071(12)60080-X ↗
21. High-throughput calculations for high-entropy alloys: a brief review10.3389/fmats.2020.00290 ↗
22. Efficient ab initio modeling of random multicomponent alloys10.1103/PhysRevLett.116.105501 ↗
23. Machine learning approach to predict new multiphase high entropy alloys10.1016/j.scriptamat.2021.113804 ↗
24. Machine-learning informed prediction of high-entropy solid solution formation: beyond the Hume-Rothery rules10.1038/s41524-020-0308-7 ↗
25. Ultra-high temperature ceramics melting temperature prediction via machine learning10.1016/j.ceramint.2019.06.076 ↗
26. Machine learning in materials informatics: recent applications and prospects10.1038/s41524-017-0056-5 ↗
27. The phase selection via machine learning in high entropy alloys10.1016/j.promfg.2019.12.051 ↗
28. Machine learning for phase selection in multi-principal element alloys10.1016/j.commatsci.2018.04.003 ↗
29. Identifying descriptors for Li+ conduction in cubic Li-argyrodites via hierarchically encoding crystal structure and inferring causality10.1016/j.ensm.2021.05.033 ↗
30. Identifying chemical factors affecting reaction kinetics in Li-air battery via ab initio calculations and machine learning10.1016/j.ensm.2020.10.022 ↗
31. Rationalizing the interphase stability of Li|doped-Li7La3Zr2O12via automated reaction screening and machine learning10.1039/C9TA06748E ↗
32. Multi-scale computation methods: their applications in lithium-ion battery research and development10.1088/1674-1056/25/1/018212 ↗
33. Materials discovery and design using machine learning
34. Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors10.1016/j.scib.2021.04.029 ↗
35. Design high-entropy carbide ceramics from machine learning10.1038/s41524-021-00678-3 ↗
36. Machine learning guided discovery of super-hard high entropy ceramics10.1016/j.matlet.2021.130899 ↗
37. Designing high-entropy ceramics via incorporation of the bond-mechanical behavior correlation with the machine-learning methodology10.1016/j.xcrp.2021.100640 ↗
38. Machine learning assisted materials design and discovery for rechargeable batteries10.1016/j.ensm.2020.06.033 ↗
39. A chemical scale for crystal-structure maps10.1016/0038-1098(84)90765-8 ↗
40. Data-driven atomic environment prediction for binaries using the Mendeleev number: Part 1. Composition AB10.1016/j.jallcom.2003.08.060 ↗
41. A three-dimensional structural stability diagram for 1011 binary AB2 intermetallic compounds: II10.1016/0022-5088(84)90333-3 ↗
42. On the crystal chemistry of normal valence compounds10.1107/S0365110X59002857 ↗
43. Systematization of the stable crystal structure of all $\mathrm{AB}$-type binary compounds: a pseudopotential orbital-radii approach10.1103/PhysRevB.22.5839 ↗
44. Formation of high entropy metal diborides using arc-melting and combinatorial approach to study quinary and quaternary solid solutions10.1016/j.jeurceramsoc.2019.10.051 ↗
45. Synthesis of high-entropy diboride nanopowders via molten salt-mediated magnesiothermic reduction10.1111/jace.17184 ↗
46. High-entropy ceramic thin films; A case study on transition metal diborides10.1016/j.scriptamat.2018.02.008 ↗
47. Dissolving and stabilizing soft WB2 and MoB2 phases into high-entropy borides via boron-metals reactive sintering to attain higher hardness10.1016/j.jeurceramsoc.2020.03.063 ↗
48. Metal borides10.1039/qr9662000441 ↗
49. Pseudopotential and all‐electron atomic core size scales10.1063/1.441556 ↗
50. TiVCrNiZrFex High entropy alloy: phase evolution,magnetic and mechanical properties10.1016/j.jallcom.2021.159572 ↗
51. Machine learning made easy: a review of scikit-learn package in Python programming language10.3102/1076998619832248 ↗
52. Structure, stability, and properties of high-entropy alloys10.1134/S0031918X20080098 ↗
53. Model predictions for the enthalpy of formation of transition metal alloys II10.1016/0364-5916(83)90030-5 ↗
54. A new perspective to thermodynamical designing of high entropy bulk metallic glasses (HE-BMGs)10.1016/j.physb.2020.412350 ↗
55. A perspective on challenges and opportunities in developing high entropy-ultra high temperature ceramics10.1016/j.ceramint.2020.07.066 ↗
56. Thirteen ways to look at the correlation coefficient10.2307/2685263 ↗
57. Multi-layer feature selection incorporating weighted score-based expert knowledge toward modeling materials with targeted properties10.1002/adts.201900215 ↗
58. Scikit-learn: machine learning in {P}ython
59. The effect of principal component analysis on machine learning accuracy with high dimensional spectral data
60. Small-sample precision of ROC-related estimates10.1093/bioinformatics/btq037 ↗
61. Machine learning: supervised methods10.1038/nmeth.4551 ↗
62. Impact of different data types on classifier performance of random forest, Naïve Bayes, and K-nearest neighbors algorithms
63. Mushtaq · 2017