{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T17:00:15Z","timestamp":1767200415585,"version":"3.48.0"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yozgat Bozok University BAP","award":["FHD-2024-1475"],"award-info":[{"award-number":["FHD-2024-1475"]}]},{"name":"Russian Ministry of Science and Higher Education","award":["FEFN-2024-0002"],"award-info":[{"award-number":["FEFN-2024-0002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nanomaterials"],"abstract":"<jats:p>A 5\u00a0nm thick polycrystalline Ni81Fe19 film was sputter-deposited onto a circular 3-inch diameter, 390\u00a0\u03bcm thick single-crystal wafer with SiO2 surface layers. The magnetoresistance (MR) of the sample was analyzed as a function of applied DC magnetic field and temperature using the Van der Pauw technique. Magnetic measurements were carried out over a temperature range of 25 \u00b0C to 350 \u00b0C using a Lake Shore Hall Effect Measurement System (HEMS). An external magnetic field ranging from +14\u00a0kG to \u221214\u00a0kG was applied at each temperature value to observe changes in resistance. Hall coefficients and resistance were obtained by applying current in both directions with different contact configurations. Machine learning techniques, including Random Forest Regression, were employed to predict magnetoresistivity beyond 350 \u00b0C; the best-performing model achieved R2 values up to 0.9449 with MSE as low as 0.0071, and enabled Curie temperature estimation with TC\u2248590.97\u00a0\u00b0C\u00a0. This study highlights the potential of machine learning in accurately forecasting material properties beyond experimental limits, providing enhanced predictive models for the magnetoresistive behavior and critical temperature transitions of Ni81Fe19\u00a0.<\/jats:p>","DOI":"10.3390\/nano16010051","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T16:34:23Z","timestamp":1767198863000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advanced Machine Learning Models for High-Temperature Magnetoresistivity Predictions of Ni81Fe19 Monolayers"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9751-3405","authenticated-orcid":false,"given":"Tarik","family":"Akan","sequence":"first","affiliation":[{"name":"Physics Department, Yozgat Bozok University, 66100 Yozgat, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Perihan","family":"Aksu","sequence":"additional","affiliation":[{"name":"Institute of Nanotechnology, Gebze Technical University, 41400 Kocaeli, T\u00fcrkiye"},{"name":"Nanomagnetism Research and Application Center, Gebze Technical University, 41400 Kocaeli, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9525-8068","authenticated-orcid":false,"given":"Recep","family":"Sahingoz","sequence":"additional","affiliation":[{"name":"Physics Department, Yozgat Bozok University, 66100 Yozgat, T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2325-4144","authenticated-orcid":false,"given":"Feliks S.","family":"Zaseev","sequence":"additional","affiliation":[{"name":"Institute of Electronics and Telecommunications, Peter the Great Saint Petersburg Polytechnic University, Polytechnicheskaya 29, 195251 Saint-Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vladislav B.","family":"Zaalishvili","sequence":"additional","affiliation":[{"name":"Geophysical Institute\u2014The Affiliate of Vladikavkaz Scientific Centre of the Russian Academy of Sciences, Markova 93a, 362002 Vladikavkaz, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7830-4715","authenticated-orcid":false,"given":"Tamerlan T.","family":"Magkoev","sequence":"additional","affiliation":[{"name":"Geophysical Institute\u2014The Affiliate of Vladikavkaz Scientific Centre of the Russian Academy of Sciences, Markova 93a, 362002 Vladikavkaz, Russia"},{"name":"Laboratory of Physics of Adsorption Phenomena, Department of Condensed Matter Physics, North Ossetian State University, Vatutina 44-46, 362025 Vladikavkaz, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1126\/science.289.5484.1530","article-title":"Enhanced room-temperature magnetoresistance in inhomogeneous magnetic multilayers","volume":"289","author":"Solin","year":"2000","journal-title":"Science"},{"key":"ref_2","unstructured":"Wieder, H.H. (1971). Hall Generators and Magnetoresistors, Pion."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chang, Y., Wang, Y., Zhang, J., Xing, Y., Li, G., Deng, D., and Liu, L. (2022). Overview on the design of magnetically assisted electrochemical biosensors. Biosensors, 12.","DOI":"10.3390\/bios12110954"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gurney, B.A., Carey, M., Tsang, C., Williams, M., Parkin, S.S.P., Fontana, R.E., Grochowski, E., Pinarbasi, M., Lin, T., and Mauri, D. (2005). Spin valve giant magnetoresistive sensor materials for hard disk drives. Ultrathin Magnetic Structures IV: Applications of Nanomagnetism, Springer.","DOI":"10.1007\/3-540-27164-3_6"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jmmm.2003.10.019","article-title":"Structural and magnetic properties of Ni81Fe19\/Zr multilayers","volume":"277","author":"Biondo","year":"2004","journal-title":"J. Magn. Magn. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/B978-0-323-90800-9.00229-8","article-title":"Spintronic materials","volume":"Volume 2","author":"Valenzuela","year":"2024","journal-title":"Encyclopedia of Condensed Matter Physics"},{"key":"ref_7","first-page":"100853","article-title":"Crystal, ferromagnetism, and magnetoresistance with sign reversal in a EuAgP semiconductor","volume":"11","author":"Zhao","year":"2025","journal-title":"J. Mater."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4494","DOI":"10.1103\/PhysRevLett.78.4494","article-title":"Giant magnetocaloric effect in Gd5(Si2Ge2)","volume":"78","author":"Pecharsky","year":"1997","journal-title":"Phys. Rev. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3673","DOI":"10.1063\/1.323176","article-title":"Magnetic heat pumping near room temperature","volume":"47","author":"Brown","year":"1976","journal-title":"J. Appl. Phys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1038\/s41524-018-0085-8","article-title":"Machine learning modeling of superconducting critical temperature","volume":"4","author":"Stanev","year":"2018","journal-title":"npj Comput. Mater."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41524-020-0287-8","article-title":"Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning","volume":"6","author":"Court","year":"2020","journal-title":"npj Comput. Mater."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.actamat.2018.03.051","article-title":"Compositional optimization of hard-magnetic phases with machine-learning models","volume":"153","author":"Krugel","year":"2018","journal-title":"Acta Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"117106","DOI":"10.1088\/1674-1056\/ac89dd","article-title":"Switching plasticity in compensated ferrimagnetic multilayers for neuromorphic computing","volume":"31","author":"Li","year":"2022","journal-title":"Chin. Phys. B"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"012002","DOI":"10.1088\/2399-1984\/acbcb5","article-title":"Recent advances in magnetoresistance biosensors: A short review","volume":"7","author":"Dey","year":"2023","journal-title":"Nano Futures"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"76","DOI":"10.4283\/JKMS.2023.33.2.076","article-title":"Permeability measurement performance of PHR sensor according to direction of driving current","volume":"33","author":"Yoon","year":"2023","journal-title":"J. Korean Magn. Soc."},{"key":"ref_16","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"L140401","DOI":"10.1103\/PhysRevB.106.L140401","article-title":"Enhancement of room-temperature unidirectional spin Hall magnetoresistance by using a ferromagnetic metal with a low Curie temperature","volume":"106","author":"Yamanoi","year":"2022","journal-title":"Phys. Rev. B"},{"key":"ref_18","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2017). An Introduction to Statistical Learning, Springer. [8th ed.]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"011002","DOI":"10.1063\/1.4812323","article-title":"The Materials Project: A materials genome approach to accelerating materials innovation","volume":"1","author":"Jain","year":"2013","journal-title":"APL Mater."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"353002","DOI":"10.1088\/1361-6463\/abfbfb","article-title":"Current trends in planar Hall effect sensors: Evolution, optimization, and applications","volume":"54","author":"Elzwawy","year":"2021","journal-title":"J. Phys. D Appl. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"16028","DOI":"10.1038\/npjcompumats.2016.28","article-title":"A general-purpose machine learning framework for predicting properties of inorganic materials","volume":"2","author":"Ward","year":"2016","journal-title":"npj Comput. Mater."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5493","DOI":"10.1109\/JSEN.2019.2907616","article-title":"Shapeable Planar Hall Sensor With a Stable Sensitivity Under Concave and Convex Bending","volume":"19","author":"Oezer","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Firkowska, I., Giannona, S., Rojas-Chapana, J.A., Luecke, K., Br\u00fcstle, O., and Giersig, M. (2008). Biocompatible nanomaterials and nanodevices promising for biomedical applications. Nanomaterials for Application in Medicine and Biology, Springer.","DOI":"10.1007\/978-1-4020-6829-4_1"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"S208","DOI":"10.1107\/S0108767309095671","article-title":"Influence of deposition technique on growth and resistivity of Ta\/NiFe nano films","volume":"65","author":"Capar","year":"2009","journal-title":"Acta Crystallogr. A"},{"key":"ref_25","unstructured":"Kaya, A. (2019). Magnetoresistance Measurement of Thin Film with Hall Effect Technique. [Master\u2019s Thesis, Yozgat Bozok University, Graduate School of Natural and Applied Sciences, Mechatronics Engineering Department]."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1002\/aenm.201200167","article-title":"Materials challenges for high performance magnetocaloric refrigeration devices","volume":"2","author":"Smith","year":"2012","journal-title":"Adv. Energy Mater."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/s41524-019-0199-7","article-title":"High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using density functional theory","volume":"5","author":"Horton","year":"2019","journal-title":"npj Comput. Mater."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"0800130","DOI":"10.1109\/TMAG.2019.2896036","article-title":"Magnetoresistive sensor development roadmap (non-recording applications)","volume":"55","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Magn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.sna.2019.04.003","article-title":"Interface-induced enhancement of sensitivity in NiFe\/Pt\/IrMn-based planar Hall sensors with nanoTesla resolution","volume":"292","author":"Piskin","year":"2019","journal-title":"Sens. Actuators A Phys."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"173902","DOI":"10.1063\/1.5023888","article-title":"Effect of NiFeCr seed and capping layers on exchange bias and planar Hall voltage response of NiFe\/Au\/IrMn trilayer structures","volume":"123","author":"Talantsev","year":"2018","journal-title":"J. Appl. Phys."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"093910","DOI":"10.1063\/1.4943033","article-title":"Planar Hall effect bridge sensors with NiFe\/Cu\/IrMn stack optimized for self-field magnetic bead detection","volume":"119","author":"Henriksen","year":"2016","journal-title":"J. Appl. Phys."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"72811","DOI":"10.1038\/s41598-020-72811-z","article-title":"Data-driven studies of magnetic two-dimensional materials","volume":"10","author":"Rhone","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4518","DOI":"10.1109\/TMAG.2009.2023426","article-title":"Optimization of the multilayer structures for a high field-sensitivity biochip sensor based on the planar Hall effect","volume":"45","author":"Hung","year":"2009","journal-title":"IEEE Trans. Magn."},{"key":"ref_34","unstructured":"Ho, T.K. (1995, January 14\u201316). Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_35","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7885","DOI":"10.1039\/C8RA10467K","article-title":"Accurate, hysteresis-free temperature sensor for health monitoring using a magnetic sensor and pristine polymer","volume":"9","author":"Jeong","year":"2019","journal-title":"RSC Adv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Maity, T., Trodahl, H.J., Granville, S., V\u00e9zian, S., Natali, F., and Ruck, B.J. (2020). Magnetoresistance of epitaxial GdN films. J. Appl. Phys., 128.","DOI":"10.1063\/5.0022950"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"024433","DOI":"10.1103\/PhysRevB.66.024433","article-title":"Electron-magnon scattering and magnetic resistivity in 3d ferromagnets","volume":"66","author":"Raquet","year":"2002","journal-title":"Phys. Rev. B"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Tomsett, R., Raghavendra, R., Harborne, D., Alzantot, M., Cerutti, F., Srivastava, M., Preece, A., Julier, S., and Rao, R.M. (2017, January 4\u20138). Interpretability of deep learning models: A survey of results. Proceedings of the 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), San Francisco, CA, USA.","DOI":"10.1109\/UIC-ATC.2017.8397411"},{"key":"ref_40","unstructured":"Chen, C.-W. (2013). Magnetism and Metallurgy of Soft Magnetic Materials, Courier Corporation."},{"key":"ref_41","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., and Dahl, G.E. (2017, January 6\u201311). Neural message passing for quantum chemistry. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.commatsci.2012.02.005","article-title":"Aflow: An automatic framework for high-throughput materials discovery","volume":"58","author":"Curtarolo","year":"2012","journal-title":"Comput. Mater. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1042","DOI":"10.1016\/j.commatsci.2008.07.016","article-title":"Data mining and accelerated electronic structure theory as a tool in the search for new functional materials","volume":"44","author":"Ortiz","year":"2009","journal-title":"Comput. Mater. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"013001","DOI":"10.1088\/1367-2630\/12\/1\/013001","article-title":"Magnetotransport effects of ultrathin Ni80Fe20 films probed in situ","volume":"12","author":"Krzyk","year":"2010","journal-title":"New J. Phys."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1007\/s40766-021-00025-8","article-title":"Solving the strong-correlation problem in materials","volume":"44","author":"Pavarini","year":"2021","journal-title":"La Rivista del Nuovo Cimento"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"107590","DOI":"10.1016\/j.measurement.2020.107590","article-title":"Development of a very high sensitivity magnetic field sensor based on planar Hall effect","volume":"156","author":"Roy","year":"2020","journal-title":"Measurement"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Dzhun, I.O., Gerasimenko, A.V., Ezhov, A.A., Bezzubov, S.I., Rodionova, V.V., Gritsenko, C.A., and Chechenin, N.G. (2023). Temperature dependence of magnetization dynamics in Co\/IrMn and Co\/FeMn exchange biased structures. Magnetochemistry, 9.","DOI":"10.3390\/magnetochemistry9100218"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"014426","DOI":"10.1103\/PhysRevB.93.014426","article-title":"Disentangling interface and bulk contributions to the anisotropic magnetoresistance in Pt\/Co\/Pt sandwiches","volume":"93","author":"Kobs","year":"2016","journal-title":"Phys. Rev. B"}],"container-title":["Nanomaterials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-4991\/16\/1\/51\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T16:56:00Z","timestamp":1767200160000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-4991\/16\/1\/51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,30]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["nano16010051"],"URL":"https:\/\/doi.org\/10.3390\/nano16010051","relation":{},"ISSN":["2079-4991"],"issn-type":[{"value":"2079-4991","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,30]]}}}