A unified quantum framework for electrons and ions: The self-consistent harmonic approximation on a neural network curved manifold
Lorenzo Monacelli, Antonio Siciliano, Nicola Marzari

TL;DR
This paper introduces a neural network-based self-consistent harmonic approximation that unifies the treatment of electrons and ions, enabling accurate simulations of quantum systems at finite temperatures.
Contribution
It extends the self-consistent harmonic approximation to electrons using a neural network on a curved manifold, bridging a methodological gap in multi-component quantum simulations.
Findings
Successfully applied to a double-well potential, hydrogen atom, and H2 dissociation.
Capable of addressing ground- and excited-state properties, including quantum tunneling.
Provides a unified framework for electrons and ions in quantum materials.
Abstract
The numerical solution of the many-body problem of interacting electrons and ions is a key challenge in condensed matter physics, chemistry, and materials science. Traditional methods to solve the multi-component quantum Hamiltonian are usually specialized for one kind of particles -- electrons or ions -- and can suffer from a methodological gap when applied to the other ones. This work extends the self-consistent harmonic approximation, a proven successful technique for simulating quantum ions at finite temperatures in anharmonic crystals, to electrons. The approach minimizes the total free energy by optimizing an ansatz density matrix, solving a fermionic self-consistent harmonic Hamiltonian on a curved manifold parametrized through a neural network. This approach preserves an analytical expression for entropy, enabling the direct computation of free energies and phase diagrams of…
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Taxonomy
TopicsSpectroscopy and Quantum Chemical Studies
