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In this work, Artificial Neural Network was used to model the hysteresis behavior of lead zirconate titanate-lead zinc niobate (Pb(Zr1/2Ti1/2)O3−Pb(Zn1/3Nb2/3)O3or (1-x)PZT-(x)PZN mixed ferroelectric systems. The hysteresis loops were measured with varying electric filed parameters and the composition x of the mixed ferroelectrics. A knowledge-based technique, i.e. the Artificial Neural Network (ANN), was employed in modeling the hysteresis to construct the database of how field parameters and the mixed composition affect dynamic hysteresis behavior. The input data to the ANN were composition x, field amplitude E0and field frequency f, where the output data was the hysteresis area. The inputs-outputs were divided into training, validating and testing datasets for the ANN. Multilayer perceptron with back propagation training algorithm was applied in this work. Exhaustive search was used to obtain the best network algorithm that gives minimum error in the training process. With the best network, unseen input datasets were fed into the network to predict hysteresis area. From the results, the predicted and the actual data match very well over an extensive range of field parameters, where the scattering plot between the predicted and the actual area has R-squared greater than 0.99. This therefore indicates ANN capabilities in modeling dynamic-hysteresis phenomena across (1-x)PZT-(x)PZN systems even they have different ratios of structural phases at microscopic level. [ABSTRACT FROM PUBLISHER]

Abstract: In this work, the spin-transition behavior in molecular magnet was investigated via Monte Carlo simulation on Ising model with mechano-elastic interaction extension. The initial spin-arrangement took hexagonal lattice structure in two dimensions, where spin molecules situated on the hexagonal lattice points were allowed to move under spring-type elastic interaction potential. Metropolis algorithm was used to update the spin configurations and thermal hysteresis loops were recorded to extract the hysteresis properties, such as period-average magnetization, loop area, loop width and height, as functions of parameters associated to magnetic and elastic interaction in the Hamiltonian. From the Monte Carlo results, the dependence of the hysteresis loop characteristic on magnitude of energy differences and number of available states between the low spin state and the high spin state was evident. The occurrence of the cooperative effect was notable, in agreement with previous experimental investigation, when the range of Hamiltonian parameters used is appropriate. Then all the measured hysteresis characteristic were passed to the Artificial Neural Network modeling to create extensive database of how the thermal hysteresis would respond to the change of molecular magnet Hamiltonian parameters. The scattering plots between the Artificial Neural Network and the real measured results have R-square closed to one which confirms the success of Artificial Neural Network in modeling this thermal hysteresis behavior. One is therefore allowed to use this Artificial Neural Network database as a guideline to design ultra-thin-film molecular magnet application in the future. [Copyright &y& Elsevier]

This paper proposed an application of Artificial Neural Network (ANN) to concurrently model ferroelectric hysteresis properties of Barium Titanate in both single-crystal and bulk-ceramics forms. In the ANN modeling, there are 3 inputs, which are type of materials (single or bulk), field amplitude and frequency, and 1 output, which is hysteresis area. Appropriate number of hidden layer and hidden node were achieved through a search of up to 2 layers and 30 neurons in each layer. After ANN had been properly trained, a network with highest accuracy was selected. Query file of unseen input data was then input to the selected network to obtain the predicted hysteresis area. From the results, the target and predicted data were found to match very well. This therefore suggests that ANN can be successfully used to concurrently model ferroelectric hysteresis property even though the considered ferroelectrics are with different domains, grains and microscopic crystal structures. [ABSTRACT FROM AUTHOR]

In this study, the artificial neural network (ANN) was used to model ferromagnetic Ising hysteresis obtained from mean-field analysis as a case study. ANNs were trained to predict the effect of external perturbations, which are the temperature, the field amplitude and the field frequency, on the hysteresis properties, which are the hysteresis area, the remanence magnetization and the coercivity. The input data to the ANN were split into training data, testing data and validating data. Search were carried out to identify number of hidden layer and number of hidden nodes to find the best architecture with highest accuracy. After the networks had been trained, they were used to predict hysteresis properties of the unseen testing patterns of input. The predicted and the actual data were found to match very well over an extensive range. This therefore suggests a success in modeling ferromagnetic hysteresis properties using the ANN technique. [ABSTRACT FROM AUTHOR]