The electronic Schr\"odinger equation describes fundamental properties of molecules and materials, but cannot be solved exactly for larger systems than the hydrogen atom. Quantum Monte Carlo is a suitable method when high-quality approximations are sought, and its accuracy is in principle limited only by the flexibility of the used wave-function ansatz. Here we develop a deep-learning wave-function ansatz, dubbed PauliNet, which has the Hartree-Fock solution built in as a baseline, incorporates the physics of valid wave functions, and is trained using variational quantum Monte Carlo (VMC). Our deep-learning method achieves higher accuracy than comparable state-of-the-art VMC ansatzes for atoms, diatomic molecules and a strongly-correlated hydrogen chain. We anticipate that this method can reveal new physical insights and provide guidance for the design of molecules and materials where highly accurate quantum-mechanical solutions are needed, such as in transition metals and other strongly correlated systems.

研究人员提出了一种基于深度学习的波函数模型——PauliNet，它使用变分量子蒙特卡洛（VMC）进行训练，并且在原子、二元分子和氢链等电子结构计算方面优于现有技术，是中型分子系统高精确度电子结构计算新的领先方法。

电子薛定谔方程的深度神经网络解