ZHAO, Bo und ZHANG, Hongbin, 2025. High-throughput computation and machine learning modelling of magnetic moments and Mössbauer parameters for Fe-based intermetallics. Physical review. B, Condensed matter and materials physics. 2025. No. Band 111, Heft 22 (2025), Artikel-ID: 224428, p. , Heft 22 (2025), Artikel-ID: 224428. DOI 10.1103/brpz-w2 tk.
Elsevier - Harvard (with titles)Zhao, B., Zhang, H., 2025. High-throughput computation and machine learning modelling of magnetic moments and Mössbauer parameters for Fe-based intermetallics. Physical review. B, Condensed matter and materials physics , Heft 22 (2025), Artikel-ID: 224428. https://doi.org/10.1103/brpz-w2 tk
American Psychological Association 7th editionZhao, B., & Zhang, H. (ca. 2025). High-throughput computation and machine learning modelling of magnetic moments and Mössbauer parameters for Fe-based intermetallics [Electronic]. Physical review. B, Condensed matter and materials physics, Band 111, Heft 22 (2025), Artikel-ID: 224428, , Heft 22 (2025), Artikel-ID: 224428. https://doi.org/10.1103/brpz-w2 tk
Springer - Basic (author-date)Zhao B, Zhang H (2025) High-throughput computation and machine learning modelling of magnetic moments and Mössbauer parameters for Fe-based intermetallics. Physical review. B, Condensed matter and materials physics , Heft 22 (2025), Artikel-ID: 224428. https://doi.org/10.1103/brpz-w2 tk
Juristische Zitierweise (Stüber) (Deutsch)Zhao, Bo/ Zhang, Hongbin, High-throughput computation and machine learning modelling of magnetic moments and Mössbauer parameters for Fe-based intermetallics, Physical review. B, Condensed matter and materials physics 2025, , Heft 22 (2025), Artikel-ID: 224428.