Modal Backflow Neural Quantum States for Anharmonic Vibrational Calculations
Lexin Ding, Markus Reiher
TL;DR
This paper introduces a new neural network design for solving quantum problems involving vibrations with high accuracy.
Contribution
The novel contribution is the development of modal backflow neural quantum states for efficient and accurate vibrational calculations.
Findings
The MBF network achieves spectroscopically accurate results for zero-point energies and vibrational transitions.
A selected-configuration scheme improves accuracy in spectroscopic calculations over traditional Monte Carlo methods.
The MBF design enables efficient pretraining via vibrational self-consistent field calculations.
Abstract
Neural quantum states (NQS) are a promising ansatz for solving many-body quantum problems due to their inherent expressiveness. Yet this expressiveness can only be harnessed efficiently for treating identical particles if the suitable physical knowledge is hardwired into the neural network itself. For electronic structure, NQS based on backflow determinants have been shown to be a powerful ansatz for capturing strong correlation. By contrast, the analogue for bosons, backflow permanents, is unpractical due to the steep cost of computing the matrix permanent and due to the lack of particle conservation in common bosonic problems. To circumvent these obstacles, we introduce a modal backflow (MBF) NQS design and demonstrate its efficacy by solving the anharmonic vibrational problem. To accommodate the demand of high accuracy in spectroscopic calculations, we implement a…
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Taxonomy
TopicsQuantum many-body systems · Machine Learning in Materials Science · Advanced Physical and Chemical Molecular Interactions