Machine learning modeling of lattice constants for half-Heusler alloys
Source abstract
Abstract
The Gaussian process regression model is developed as a machine learning tool to find statistical correlations among lattice constants, a0, of half-Heusler compounds, ionic radii, and Pauling electronegativity of their alloying elements. Nearly 140 half-Heusler samples, containing alloying elements of Cr, Mn, Fe, Co, Ni, Rh, Ti, V, Al, Ga, In, Si, Ge, Sn, P, As, and Sb, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants of half-Heusler compounds.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Heusler alloys: electronic and magnetic properties · X-ray Diffraction in Crystallography
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
North Carolina State University , Raleigh, North Carolina 27695, USA
References (32)
Show source-provided references
1. Heusler compounds—A material class with exceptional properties10.1109/tmag.2010.2096229 ↗
2. Recent advances in thermoelectric performance of half-Heusler compounds10.3390/met8120989 ↗
3. High efficiency half-Heusler thermoelectric materials for energy harvesting10.1002/aenm.201500588 ↗
4. Half-heusler ternary compounds as new multifunctional experimental platforms for topological quantum phenomena10.1038/nmat2771 ↗
5. Superconductivity in palladium-based Heusler compounds10.1103/physrevb.79.064508 ↗
6. I–II–V and I–III–IV half-Heusler compounds for optoelectronic applications: Comparative ab initio study10.1016/j.jallcom.2013.10.046 ↗
7. Opto-electric investigation for Si/organic heterojunction single-nanowire solar cells10.1038/s41598-017-15300-0 ↗
8. Engineering of hole-selective contact for high-performance perovskite solar cell featuring silver back-electrode10.1007/s10853-018-03258-x ↗
9. Crossover from semiconductor to magnetic metal in semi-Heusler phases as a function of valence electron concentration10.1088/0953-8984/10/5/011 ↗
10. Electrical transport properties of TiCoSb half-Heusler phases that exhibit high resistivity10.1088/0953-8984/13/1/308 ↗
11. Covalent bonding and the nature of band gaps in some half-Heusler compounds10.1088/0022-3727/39/5/s02 ↗
12. Transport properties of pure and doped MNiSn (M = Zr, Hf)10.1103/physrevb.59.8615 ↗
13. Computational investigation of half-Heusler compounds for spintronics applications10.1103/physrevb.95.024411 ↗
14. Electronic structure and chemical bonding in half-Heusler phases10.1016/j.jallcom.2006.08.316 ↗
15. Superconductivity in the half-Heusler compound TbPdBi10.1103/physrevb.97.224511 ↗
16. Formation of bismuth strontium calcium copper oxide superconductors
17. High critical current density Bi2Sr2CaCu2Ox/Ag wire containing oxide precursor synthesized from nano-oxides10.1088/0953-2048/29/9/095012 ↗
18. Formation of Bi2Sr2CaCu2Ox/Ag multifilamentary metallic precursor powder-in-tube wires10.1088/0953-2048/29/12/125005 ↗
19. Synthesis of Bi2Sr2CaCu2Ox superconductors via direct oxidation of metallic precursors10.1088/0953-2048/27/5/055016 ↗
20. Tunable multifunctional topological insulators in ternary Heusler compounds10.1038/nmat2770 ↗
21. Giant magnetoelectric effect in self-biased laminates under zero magnetic field10.1063/1.4794056 ↗
22. Geometry-induced magnetoelectric effect enhancement and noise floor reduction in metglas/piezofiber sensors10.1063/1.4737906 ↗
23. Half-Heusler compounds: Novel materials for energy and spintronic applications10.1088/0268-1242/27/6/063001 ↗
24. Topological electronic structure in half-Heusler topological insulators10.1103/physrevb.82.125208 ↗
25. Generalized gradient approximation made simple10.1103/physrevlett.77.3865 ↗
26. Prediction of lattice constant in cubic perovskites10.1016/j.jpcs.2006.02.004 ↗
27. Comment on “prediction of lattice constant in cubic perovskites”10.1016/j.jpcs.2007.03.050 ↗
28. Yttrium barium copper oxide superconducting transition temperature modeling through Gaussian process regression10.1016/j.commatsci.2020.109583 ↗
29. Predicting doped MgB2 superconductor critical temperature from lattice parameters using Gaussian process regression
30. Machine learning the magnetocaloric effect in manganites from lattice parameters
31. Machine learning the magnetocaloric effect in manganites from compositions and structural parameters10.1063/1.5144241 ↗
32. Artificial neural networks and vector regression models for prediction of lattice constants of half-Heusler compounds10.1088/2053-1591/aafa9f ↗