Analytical modelling of a magnetization curve obtained by the measurements of magnetic materials’ properties using evolutionary algorithms

Jesenik, Marko; Bekovi, Milo; Hamler, Anton; Trlep, M.

doi:10.1016/j.asoc.2016.10.027

Cited by 12 publications

(3 citation statements)

References 63 publications

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“…The routine begins with parameter initialization, with high accuracy possible with appropriate initial values. 52 The result of the background subtraction and LM fitting option for P1 is included in Figure 4c and is also found to be in agreement with the VSM result. A second automated fitting was attempted on the data following background subtraction using a singular value decomposition (SVD), 53 which does not rely on an estimate of starting points and is reported to extract useable dipole forms from noisy data or weak signals with high accuracy.…”

Section: ■ Introductionsupporting

confidence: 77%

“…This is the same algorithm implemented by the MPMS3 for dipole fitting and is widely used for curve fitting. The routine begins with parameter initialization, with high accuracy possible with appropriate initial values . The result of the background subtraction and LM fitting option for P1 is included in Figure c and is also found to be in agreement with the VSM result.…”

Section: Resultssupporting

confidence: 54%

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Magnetic Characterization of Open-Shell Donor–Acceptor Conjugated Polymers

et al. 2022

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Donor–acceptor (DA) conjugated polymers (CPs) with narrow bandgaps and open-shell electronic structures offer a fundamentally new paradigm for integrating the spin degree of freedom within emerging functional devices. Recent advancements have demonstrated that control of long-range electronic correlations enables low-spin (S = 0) and high-spin (S = 1) DA CPs, in which extended π-conjugation overcomes the intrinsic instability of these electronic configurations in light-element materials. While design strategies that articulate mechanisms of spin alignment, topology control, and quantum mechanical exchange are emerging, dedicated studies of the magnetic behavior of these materials remain rare. Here, we utilize sensitive magnetometry techniques to analyze the magnetic properties of open-shell DA CPs with low- and high-spin ground states. We demonstrate improved measurement accuracy through combining vibrating sample magnetometry and superconducting quantum interference device magnetometry. This serves to overcome challenges associated with the inherently weak magnetic moments of these materials and a measurement environment in which the background signal is always significant and must be carefully removed. Analyzing the results following established models for paramagnetic materials enables precise quantification of the spin quantum number and temperature-dependent spin alignment. These studies articulate approaches that enable precise characterization of the bulk magnetic features of these heterogeneous and disordered materials systems, providing a path for rational property elucidation that will enable the integration of these materials within emerging technologies.

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Section: ■ Introductionsupporting

confidence: 77%

Section: Resultssupporting

confidence: 54%

Magnetic Characterization of Open-Shell Donor–Acceptor Conjugated Polymers

et al. 2022

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“…end-for 18. until a termination condition is met For all tests we used 83,160 FEs, which were appropriate in [66] for nine parameters, and in this work we are dealing with five (D = 5) or six parameters (D = 6). The population size is ten times the number of parameters, fifty for five parameters and sixty for six parameters.…”

Section: Calculation Of the Parameters Using Evolutionary Methodsmentioning

confidence: 99%

Determination of a Hysteresis Model Parameters with the Use of Different Evolutionary Methods for an Innovative Hysteresis Model

2020

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For precise modeling of electromagnetic devices, we have to model material hysteresis. A Genetic Algorithm, Differential Evolution with three different strategies, teaching–learning-based optimization and Artificial Bee Colony, were used for testing seven different modified mathematical expressions, and the best combination of mathematical expression and solving method was used for hysteresis modeling. The parameters of the hysteresis model were determined based on the measured major hysteresis loop and first-order reversal curves. The model offers a simple determination of the magnetization procedure in the areas between measured curves, with the only correction of two parameters based on only two known points in the magnetization process. It was tested on two very different magnetic materials, and results show good agreement between the measured and calculated curves. The calculated curves between the measured curves have correct shapes. The main difference between our model and other models is that, in our model, each measured curve, major and reversal, is described with different parameters. The magnetization process between measured curves is described according to the nearest measured curve, and this ensures the best fit for each measured curve. In other models, there is mainly only one curve, a major hysteresis or magnetization curve, used for the determination of the parameters, and all other curves are then dependent on this curve. Results confirm that the evolutionary optimization method offers a reliable procedure for precise determination of the parameters.

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An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry

Zhou

et al. 2017

Applied Soft Computing

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Analytical modelling of a magnetization curve obtained by the measurements of magnetic materials’ properties using evolutionary algorithms

Cited by 12 publications

References 63 publications

Magnetic Characterization of Open-Shell Donor–Acceptor Conjugated Polymers

Magnetic Characterization of Open-Shell Donor–Acceptor Conjugated Polymers

Determination of a Hysteresis Model Parameters with the Use of Different Evolutionary Methods for an Innovative Hysteresis Model

An improved teaching-learning-based optimization algorithm and its application to a combinatorial optimization problem in foundry industry

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