We have recently developed a new micromagnetie method at finite temperature, where the Hybrid Monte Carlo method is employed to realize the Boltzmann distribution with respect to the magnetic free energy. Hence, the h...We have recently developed a new micromagnetie method at finite temperature, where the Hybrid Monte Carlo method is employed to realize the Boltzmann distribution with respect to the magnetic free energy. Hence, the hysteresis loops and domain structures at arbitrary temperature below the Curie point Tc can be simulated. The Haxnilton equations are used to find the magnetization distributions instead of the Landau-Lifshitz （LL） equations. In our previous work, we applied this method on a simple uniaxial anisotropy nano-paxticle and compared it with the mieromagnetic method using LL equations. In this work, we use this new method to simulate an LIO FePt-C granular thin film at finite temperatures. The polycrystalline Voronoi microstructure is included in the model, and the effects of the misorientation of FePt grains are also simulated.展开更多
Magnetization switching is one of the most fundamental topics in the field of magnetism.Machine learning(ML)models of random forest(RF),support vector machine(SVM),deep neural network(DNN)methods are built and trained...Magnetization switching is one of the most fundamental topics in the field of magnetism.Machine learning(ML)models of random forest(RF),support vector machine(SVM),deep neural network(DNN)methods are built and trained to classify the magnetization reversal and non-reversal cases of single-domain particle,and the classification performances are evaluated by comparison with micromagnetic simulations.The results show that the ML models have achieved great accuracy and the DNN model reaches the best area under curve(AUC)of 0.997,even with a small training dataset,and RF and SVM models have lower AUCs of 0.964 and 0.836,respectively.This work validates the potential of ML applications in studies of magnetization switching and provides the benchmark for further ML studies in magnetization switching.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos 51171086 and 51371101
文摘We have recently developed a new micromagnetie method at finite temperature, where the Hybrid Monte Carlo method is employed to realize the Boltzmann distribution with respect to the magnetic free energy. Hence, the hysteresis loops and domain structures at arbitrary temperature below the Curie point Tc can be simulated. The Haxnilton equations are used to find the magnetization distributions instead of the Landau-Lifshitz （LL） equations. In our previous work, we applied this method on a simple uniaxial anisotropy nano-paxticle and compared it with the mieromagnetic method using LL equations. In this work, we use this new method to simulate an LIO FePt-C granular thin film at finite temperatures. The polycrystalline Voronoi microstructure is included in the model, and the effects of the misorientation of FePt grains are also simulated.
文摘Magnetization switching is one of the most fundamental topics in the field of magnetism.Machine learning(ML)models of random forest(RF),support vector machine(SVM),deep neural network(DNN)methods are built and trained to classify the magnetization reversal and non-reversal cases of single-domain particle,and the classification performances are evaluated by comparison with micromagnetic simulations.The results show that the ML models have achieved great accuracy and the DNN model reaches the best area under curve(AUC)of 0.997,even with a small training dataset,and RF and SVM models have lower AUCs of 0.964 and 0.836,respectively.This work validates the potential of ML applications in studies of magnetization switching and provides the benchmark for further ML studies in magnetization switching.
