Towards the ground state of molecules via diffusion Monte Carlo on neural networks

Abstract

Diffusion Monte Carlo (DMC) based on fixed-node approximation has enjoyed significant developments in the past decades and become one of the go-to methods when accurate ground state energy of molecules and materials is needed. The remaining bottleneck is the limitations of the inaccurate nodal structure, prohibiting more challenging electron correlation problems to be tackled with DMC. In this work, we apply the neural-network based trial wavefunction in fixed-node DMC, which allows accurate calculation of a broad range of atomic and molecular systems of different electronic characteristics. Our method is superior in both accuracy and efficiency compared to state-of-the-art neural network methods using variational Monte Carlo. Overall, this computational framework provides a new benchmark for accurate solution of correlated electronic wavefunction and also shed light on the chemical understanding of molecules.

References in corpus (3)

• Inhomogeneous backflow transformations in quantum Monte Carlo calculations
• Ab initio calculation of real solids via neural network ansatz
• Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation

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• Optimizing Temperature Distributions for Training Neural Quantum States using Parallel Tempering
• Assessing Orbital Optimization in Variational and Diffusion Monte Carlo