Learning the Effective Spin Hamiltonian of a Quantum Magnet

• 作者: Sizhuo Yu Yuan Gao Bin-Bin Chen Wei Li
• 来源期刊: 中国物理快报（英文版）
• 年，卷（期）: 2021,（9）
• 所属期刊栏目: CONDENSED MATTER: ELECTRONIC STRUCTURE, ELECTRICAL,MAGNETIC, AND OPTICAL PROPERTIES
• 页码范围: 139-157
• 页数: 19页
• 语种: 英文
• DOI: 10.1088/0256-307X/38/9/097502

摘要

To understand the intriguing many-body states and effects in the correlated quantum materials,inference of the microscopic effective Hamiltonian from experiments constitutes an important yet very challenging inverse problem.Here we propose an unbiased and efficient approach learning the effective Hamiltonian through the many-body analysis of the measured thermal data.Our approach combines the strategies including the automatic gradient and Bayesian optimization with the thermodynamics many-body solvers including the exact diagonalization and the tensor renormalization group methods.We showcase the accuracy and powerfulness of the Hamiltonian learning by applying it firstly to the thermal data generated from a given spin model,and then to realistic experimental data measured in the spin-chain compound copper nitrate and triangular-lattice magnet TmMgGaO4.The present automatic approach constitutes a unified framework of many-body thermal data analysis in the studies of quantum magnets and strongly correlated materials in general.