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An automated algorithm for reliable equation of state fitting of magnetic systems
Royal Inst Technol, Appl Mat Phys, Dept Mat Sci & Engn, SE-10044 Stockholm, Sweden.
Royal Inst Technol, Appl Mat Phys, Dept Mat Sci & Engn, SE-10044 Stockholm, Sweden.
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory. Royal Inst Technol, Appl Mat Phys, Dept Mat Sci & Engn, SE-10044 Stockholm, Sweden;Hungarian Acad Sci, Wigner Res Ctr Phys, Inst Solid State Phys & Opt, POB 49, H-1525 Budapest, Hungary.ORCID iD: 0000-0003-2832-3293
Univ Turku, Dept Phys & Astron, FI-20014 Turku, Finland;Tampere Univ Technol, Dept Phys, FI-33101 Tampere, Finland.
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 SCIENCE BV , 2019. Vol. 156, p. 121-128
Keywords [en]
Equation of state fitting, EOS
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-369378DOI: 10.1016/j.commatsci.2018.09.026ISI: 000449375500015OAI: oai:DiVA.org:uu-369378DiVA, id: diva2:1278734
Available from: 2019-01-15 Created: 2019-01-15 Last updated: 2019-01-15Bibliographically approved

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