Xie, Ruiwen ; Zhang, Yixuan ; Li, Fu ; Li, Zhiyuan ; Zhang, Hongbin (2023)
Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties.
In: ArXiv. Condensed Matter, Material Science
doi: 10.48550/arXiv.2309.04168
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Designing materials with enhanced spin charge conversion, i.e., with high spin Hall conductivity (SHC) and low longitudinal electric conductivity (hence large spin Hall angle (SHA)), is a challenging task, especially in the presence of a vast chemical space for compositionally complex alloys (CCAs). In this work, focusing on the Ta-Nb-Hf-Zr-Ti system, we confirm that CCAs exhibit significant spin Hall conductivities and propose a multi-objective Bayesian optimization approach (MOBO) incorporated with active learning (AL) in order to screen for the optimal compositions with significant SHC and SHA. As a result, within less than 5 iterations we are able to target the TaZr-dominated systems displaying both high magnitudes of SHC (~-2.0 (10−3 Ω cm)−1) and SHA (~0.03). The SHC is mainly ascribed to the extrinsic skew scattering mechanism. Our work provides an efficient route for identifying new materials with significant SHE, which can be straightforwardly generalized to optimize other properties in a vast chemical space.
Typ des Eintrags: | Artikel |
---|---|
Erschienen: | 2023 |
Autor(en): | Xie, Ruiwen ; Zhang, Yixuan ; Li, Fu ; Li, Zhiyuan ; Zhang, Hongbin |
Art des Eintrags: | Bibliographie |
Titel: | Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties |
Sprache: | Englisch |
Publikationsjahr: | 8 September 2023 |
Ort: | Cornell University |
Titel der Zeitschrift, Zeitung oder Schriftenreihe: | ArXiv. Condensed Matter, Material Science |
DOI: | 10.48550/arXiv.2309.04168 |
URL / URN: | https://arxiv.org/abs/2309.04168 |
Kurzbeschreibung (Abstract): | Designing materials with enhanced spin charge conversion, i.e., with high spin Hall conductivity (SHC) and low longitudinal electric conductivity (hence large spin Hall angle (SHA)), is a challenging task, especially in the presence of a vast chemical space for compositionally complex alloys (CCAs). In this work, focusing on the Ta-Nb-Hf-Zr-Ti system, we confirm that CCAs exhibit significant spin Hall conductivities and propose a multi-objective Bayesian optimization approach (MOBO) incorporated with active learning (AL) in order to screen for the optimal compositions with significant SHC and SHA. As a result, within less than 5 iterations we are able to target the TaZr-dominated systems displaying both high magnitudes of SHC (~-2.0 (10−3 Ω cm)−1) and SHA (~0.03). The SHC is mainly ascribed to the extrinsic skew scattering mechanism. Our work provides an efficient route for identifying new materials with significant SHE, which can be straightforwardly generalized to optimize other properties in a vast chemical space. |
ID-Nummer: | arXiv:2309.04168 |
Zusätzliche Informationen: | We appreciate the funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -Project-ID 405553726 – TRR 270. The Lichtenberg high-performance computer of TU Darmstadt is gratefully ac- knowledged for providing computational resources for all the calculations carried out in this work. |
Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Theorie magnetischer Materialien |
Hinterlegungsdatum: | 19 Jun 2024 08:50 |
Letzte Änderung: | 19 Jun 2024 08:50 |
PPN: | |
Export: | |
Suche nach Titel in: | TUfind oder in Google |
![]() |
Frage zum Eintrag |
Optionen (nur für Redakteure)
![]() |
Redaktionelle Details anzeigen |