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An automated algorithm for reliable equation of state fitting of magnetic systems
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Applied Material Physics.
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering.
KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Applied Material Physics. KTH, School of Engineering Sciences (SCI), Applied Physics.ORCID iD: 0000-0003-2832-3293
2019 (English)In: Computational materials science, ISSN 0927-0256, E-ISSN 1879-0801, Vol. 156, p. 121-128Article in journal (Refereed) Published
Abstract [en]

In computational physics and materials science ground-state properties are often extracted from an equation of state fit to energy-volume data. Magnetic systems often have multiple magnetic phases present in the energy-volume data, which poses a challenge for the fitting approach because the results are sensitive to the selection of included fitting points. This is because practically all popular equation of state fitting functions, such as Murnaghan and Birch-Murnaghan, assume just one phase and therefore cannot correctly fit magnetic energy-volume data that contains multiple phases. When fitting magnetic energy-volume data it is therefore important to select the range of fitting points in such a way that only points from the one relevant phase are included. We present a simple algorithm that makes the point selection automatically. Selecting fitting points automatically removes human bias and should also be useful for large-scale projects where selecting all fitting points by hand is not feasible.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 156, p. 121-128
Keywords [en]
EOS, Equation of state fitting, Ground state, Magnetism, Automated algorithms, Computational physics, Equation of state, Fitting functions, Ground state properties, Large-scale projects, Magnetic energies, SIMPLE algorithm, Equations of state
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-236337DOI: 10.1016/j.commatsci.2018.09.026ISI: 000449375500015Scopus ID: 2-s2.0-85053772857OAI: oai:DiVA.org:kth-236337DiVA, id: diva2:1262143
Note

QC 20181109

Available from: 2018-11-09 Created: 2018-11-09 Last updated: 2022-06-26Bibliographically approved

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Levämäki, HenrikTian, YeVitos, Levente

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CiteExportLink to record
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