Disorder Identification in Hysteresis Data: Recognition Analysis of the Random-Bond-Random-Field Ising Model

Journal Article · Fri Oct 09 00:00:00 EDT 2009 · Physical Review Letters

DOI:https://doi.org/10.1103/PHYSREVLETT.103.157203· OSTI ID:21370750

Ovchinnikov, O S ^[1]; Jesse, S; Kalinin, S V ^[2]; Bintacchit, P; Trolier-McKinstry, S ^[3]

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1. Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996 (United States)
2. Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (United States)
3. Department of Materials Science and Engineering and Materials Research Institute, Pennsylvania State University, University Park, Pennsylvania 16802 (United States)

An approach for the direct identification of disorder type and strength in physical systems based on recognition analysis of hysteresis loop shape is developed. A large number of theoretical examples uniformly distributed in the parameter space of the system is generated and is decorrelated using principal component analysis (PCA). The PCA components are used to train a feed-forward neural network using the model parameters as targets. The trained network is used to analyze hysteresis loops for the investigated system. The approach is demonstrated using a 2D random-bond-random-field Ising model, and polarization switching in polycrystalline ferroelectric capacitors.

• OSTI ID: 21370750
• Journal Information: Physical Review Letters, Vol. 103, Issue 15; Other Information: DOI: 10.1103/PhysRevLett.103.157203; (c) 2009 The American Physical Society; ISSN 0031-9007
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
CAPACITORS
FERROELECTRIC MATERIALS
HYSTERESIS
ISING MODEL
NEURAL NETWORKS
POLARIZATION
POLYCRYSTALS
RANDOMNESS
CRYSTAL MODELS
CRYSTALS
DIELECTRIC MATERIALS
ELECTRICAL EQUIPMENT
EQUIPMENT
MATERIALS
MATHEMATICAL MODELS