This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Article
Identification of stable alloy with giant magnetization via machine learning based autonomous materials search
Yuma Iwasaki
NEC (Japan)
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. Currently, the binary alloy Fe3Co1 exhibits the largest magnetization of any stable alloys explained by the Slater-Pauling rule. A multi-element alloy system has been expected to include an alloy with magnetization beyond that of Fe3Co1, but it has been difficult to identify appropriate elements and compositions because of combinatorial explosion in the multi-element system. In this work, we found a new alloy with giant magnetization beyond that of Fe3Co1 via an autonomous materials search system combining machine learning and ab-initio calculation. After autonomous/automated exploration in the large material space of multi-element alloy for six weeks, the system unexpectedly indicated that Ir and/or Pt impurities would enhance the magnetization of FeCo alloys. To confirm this experimentally, we comprehensively synthesized FexCoyIr1-x-y and FexCoyPt1-x-y alloys and found that some of them have magnetization beyond that of Fe3Co1.
Keywords
Fe3Co1, alloys, magnetization
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
- FigS1.png
The Slater-Pauling curve. A graphical summary of the magnetic moments (∝ magnetization) in a binary alloy system calculated ab-initio using the KKR-CPA method. The Fe75Co25 alloy, which is located at the top of the curve, has the largest magnetic moment.
- FigS2.png
Local magnetic moment in Fe99X1 binary system. To limit the material search space, we calculated local magnetic moments of various impurities X in Fe (Fe99X1) using the ab-initio (KKR) method. Positive and negative signs indicate parallel and anti-parallel magnetic moments of element X with respect to Fe local magnetic moment. We identified eight elements (Fe, Co, Ni, Ru, Rh, Pd, Ir and Pt) that have local magnetic moments greater than +0.25 μB.
- FigS3.png
Combinatorial MOKE data. Results of combinatorial MOKE experiments. a, b, c, Composition maps of FexCoyIr100-x-y, FexCoyPt100-x-y and FexCoyNi100-x-y composition-spread thin films respectively. d,e,f, MOKE curve data of FexCoyIr100-x-y, FexCoyPt100-x-y and FexCoyNi100-x-y composition-spread thin films, respectively. Inserting Ir and Pt impurities enhances not only magnetization but also coercive force. g,MOKE curves of Fe67.3Co26.3Pt6.4.
- FigS4.png
Results of combinatorial XRD experiments. a, b, c, Composition maps of FexCoyIr100-x-y, FexCoyPt100-x-y and FexCoyNi100-x-y composition-spread thin films. d,e,f, XRD curves of FexCoyIr100- x-y, FexCoyPt100-x-y and FexCoyNi100-x-y composition-spread thin films. g, XRD curve of Fe67.3Co26.3Pt6.4. There are no diffraction peaks in the area of 2θ>75°. All XRD curves include a small diffraction peak at 2θ=61.7° fromthe X-ray source (Cu-Kβ). All samples have an Fe (200) peak around 2θ>65° although its intensity decreases with increasing amounts of Ir, Pt and Ni impurities.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 14 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
This is a preprint. It has not completed peer review.
Article
Identification of stable alloy with giant magnetization via machine learning based autonomous materials search
Yuma Iwasaki
NEC (Japan)
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 14 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. Currently, the binary alloy Fe3Co1 exhibits the largest magnetization of any stable alloys explained by the Slater-Pauling rule. A multi-element alloy system has been expected to include an alloy with magnetization beyond that of Fe3Co1, but it has been difficult to identify appropriate elements and compositions because of combinatorial explosion in the multi-element system. In this work, we found a new alloy with giant magnetization beyond that of Fe3Co1 via an autonomous materials search system combining machine learning and ab-initio calculation. After autonomous/automated exploration in the large material space of multi-element alloy for six weeks, the system unexpectedly indicated that Ir and/or Pt impurities would enhance the magnetization of FeCo alloys. To confirm this experimentally, we comprehensively synthesized FexCoyIr1-x-y and FexCoyPt1-x-y alloys and found that some of them have magnetization beyond that of Fe3Co1.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.