Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo
This work addresses a bottleneck in ab initio quantum chemistry for researchers, enabling broader applications to atoms, molecules, and reactions, though it is incremental as it builds on existing NN-VMC methods.
The authors tackled the high computational cost of neural network-based variational Monte Carlo (NN-VMC) by developing the Forward Laplacian framework, which speeds up computation by more than one order of magnitude and extends applicability to larger chemical systems.
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.