Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches hinders their applications in realistic chemistry problems. Here, we report the development of a new NN-VMC method that achieves a remarkable speed-up by more than one order of magnitude, thereby greatly extending the applicability of NN-VMC to larger systems. Our key design is a novel computational framework named Forward Laplacian, which computes the Laplacian associated with neural networks, the bottleneck of NN-VMC, through an efficient forward propagation process. We then demonstrate that Forward Laplacian is not only versatile but also facilitates more developments of acceleration methods across various aspects, including optimization for sparse derivative matrix and efficient neural network design. Empirically, our approach enables NN-VMC to investigate a broader range of atoms, molecules and chemical reactions for the first time, providing valuable references to other ab initio methods. The results demonstrate a great potential in applying deep learning methods to solve general quantum mechanical problems.

我们开发了一种名为Forward Laplacian的新型计算框架，通过高效的前向传播过程计算神经网络的Laplacian，从而极大地提高了NN-VMC的适用性，从而使NN-VMC能够在更大的系统中调查广泛的原子、分子和化学反应，为解决一般量子机械问题提供了巨大的潜力。

前向拉普拉斯算子：一种基于神经网络变分蒙特卡罗的新型计算框架