The neural-network quantum states (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 the computational problem size while maintaining high accuracy. We have 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.

神经网络量子态（NNQS）方法正在迅速成为量子机制中的强大工具。虽然使用 NNQS 模拟简单分子方面已经取得了重大进展，但复杂固态材料的从头计算仍然具有挑战性。在这项工作中，我们采用周期密度矩阵嵌入理论来扩展 NNQS 方法来处理复杂的固态系统。我们的方法显着减少了计算问题的规模，同时保持了高精度。我们根据传统方法和扩展系统中的实验数据验证了我们方法的准确性和效率，并研究了过渡金属化合物中的磁有序和电荷密度波态。我们的研究结果表明，量子嵌入与直观化学碎片的结合可以显着增强现实材料的 NNQS 模拟。