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
Monte CarloenergySchrödinger equationThe solution of Schrdinger equation generates the quantisation properties of the system, which can completely describe the quantum behaviour of microscopic particles in a physical system. However, solving the Schrdinger equation is a non-deterministic polynomial time ...
47, accurate quantum Monte Carlo results for large non-polar organics, which comprise the majority of experimentally relevant systems, are lacking. We believe that the present work fills this gap. Our results for positronium hydride, sodium and magnesium atoms, and small diatomic molecules ...
Projects Security Insights Additional navigation options main 3Branches0Tags Code README Apache-2.0 license LapNet A JAX implementation of the algorithm and calculations described inA Computational Framework for Neural Network-based Variational Monte Carlo with Forward Laplacian. ...
coupled cluster or other quantum chemistry (QC) methods. In this Review, we discuss a complementary approach using machine learning to aid the direct solution of QC problems from first principles. Specifically, we focus on quantum Monte Carlo methods that use neural-network ansatzes to solve the...
A hybrid quantum computing-based deep learning scheme is proposed in Ajagekar and You (2021) for substation and transmission line fault diagnosis showing high computational efficiency and good generalization ability with fast response time. The stacked sparse autoencoder (SSAE) based DNN has been ...
Simulating the fractional quantum Hall effect (FQHE) with neural network variational Monte Carlo. This repository contains the codebase for the paperTaming Landau level mixing in fractional quantum Hall states with deep learning. If you use this code in your work, pleasecite our paper. ...
For the quantum chemistry problem, the high-dimensional quantum wavefunction can be modeled by combining several neural network layers with a determinantal layer that enforces the Fermionic antisymmetry [3], [4]. Such neural network wavefunctions, optimized using the variational Monte Carlo method, ...
Therefore, the essential part of DFT success is based on the progress in the development of XC approximations. Traditionally, they are built upon analytic solutions in low- and high-density limits and result from quantum Monte Carlo numerical calculations. However, there is no consistent and ...
& Chen, J. Interatomic force from neural network based variational quantum Monte Carlo. J. Chem. Phys.157, 164104 (2022). 13. Scherbela, M., Reisenhofer, R., Gerard, L., Marquetand, P. & Grohs, P. Solving the electronic Schrödinger equation for multiple nuclear geometries with ...