摘要
The neural-network quantumstates(NNQS)method is rapidly emerging as a powerful tool in quantum mechanisms.While significant advancements have been achieved in simulating simple molecules using NNQS,the ab initio simulation of complex solid-state materials remains challenging.Here in this work,we have adopted the periodic density matrix embedding theory to extend the NNQS method to deal with complex solid-state systems.Our approach notably reduces thecomputational problem size while maintaining high accuracy.Wehave validated the accuracy and efficiency of our method against traditional methodologies and experimental data in extended systems,and have investigated the magnetic ordering and charge density wave state in transition metal compounds.The findings from our research indicate that the integration of quantum embedding with intuitive chemical fragmentation can significantly enhance the NNQS simulation of realistic materials.
基金
the National Natural Science Foundation of China(T2222026,22288201)
Innovation Program for Quantum Science and Technology(2021ZD0303306).
