Machine Learning Magnetic Parameters from Spin Configurations

Advanced Science

Anran Yuan¹  Songrui Wei²  Kaiyan Cao³  Dingchen Wang³  Fanghua Tian³  Chao Zhou³  Qizhong Zhao³  Yin Zhang³  Sen Yang³  Xiaoping Song³  Dezhen Xue³ 

[1] Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation School of Artificial Intelligence Xidian University Xi'an 710071 China;Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Optoelectronic Engineering Shenzhen University Shenzhen 518060 China;MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of Science State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong University Xi'an 710049 China;

关键词: machine learning;    micro‐magnetism;    parameter estimation;    spin configurations;   

DOI  :  10.1002/advs.202000566

来源: DOAJ

【 摘 要 】

Abstract Hamiltonian parameters estimation is crucial in condensed matter physics, but is time‐ and cost‐consuming. High‐resolution images provide detailed information of underlying physics, but extracting Hamiltonian parameters from them is difficult due to the huge Hilbert space. Here, a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single unexplored experimental image to estimate its key parameters, and predicting the corresponding materials properties by a physical model. The efficiency of the approach is demonstrated by reproducing the same spin configuration as the experimental one and predicting the coercive field, the saturation field, and even the volume of the experiment specimen accurately. The proposed approach paves a way to achieve a stable and efficient parameters estimation.

【 授权许可】

Unknown