Active Learning approach to simulations of Strongly Correlated Matter with the Ghost Gutzwiller Approximation
arXiv:2312.05343 · doi:10.1103/PhysRevResearch.6.013242
Abstract
Quantum embedding (QE) methods such as the Ghost Gutzwiller Approximation (gGA) offer a powerful approach to simulating strongly-correlated systems, but come with the computational bottleneck of computing the ground state of an auxiliary embedding Hamiltonian (EH) iteratively. In this work, we introduce an active learning (AL) framework integrated within the gGA to address this challenge. The methodology is applied to the single-band Hubbard model and results in a significant reduction in the number of instances where the EH must be solved. Through a principal component analysis (PCA), we find that the EH parameters form a low-dimensional structure that is largely independent of the geometric specifics of the systems, especially in the strongly-correlated regime. Our AL strategy enables us to discover this low-dimensionality structure on the fly, while leveraging it for reducing the computational cost of gGA, laying the groundwork for more efficient simulations of complex strongly-correlated materials.
arXiv comment14 pages, 13 figures
References in corpus (25)
- Electronic Structure Calculations with Dynamical Mean-Field Theory: A Spectral Density Functional Approach
- First-principles Calculations of the Electronic Structure and Spectra of Strongly Correlated Systems: Dynamical Mean-field Theory
- Density matrix embedding: A simple alternative to dynamical mean-field theory
- Quantum embedding theories
- A practical guide to density matrix embedding theory in quantum chemistry
- Rotationally-invariant slave-boson formalism and momentum dependence of the quasiparticle weight
- Machine learning for many-body physics: The case of the Anderson impurity model
- LDA+Gutzwiller Method for Correlated Electron Systems: Formalism and Its Applications
- Efficient implementation of the Gutzwiller variational method
- Gutzwiller density functional theory for correlated electron systems
- Slave Boson Theory of Orbital Differentiation with Crystal Field Effects: Application to UO
- Dynamical Mean Field Theory, Density-Matrix Embedding Theory and Rotationally Invariant Slave Bosons: a Unified Perspective
- Emergent Bloch Excitations in Mott Matter
- Machine learning for molecular dynamics with strongly correlated electrons
- Rotationally invariant slave-boson and density matrix embedding theory: A unified framework and a comparative study on the 1D and 2D Hubbard Model
- Quantum-embedding description of the Anderson lattice model with the ghost Gutzwiller Approximation
- Properties of Gutzwiller wave-functions for multi-band models
- Accuracy of ghost-rotationally-invariant slave-boson and dynamical mean field theory as a function of the impurity-model bath size
- Efficient computational screening of strongly correlated materials -- Multi-orbital phenomenology within the ghost Gutzwiller approximation
- Mott transition in a metallic liquid - Gutzwiller molecular dynamics simulations
- Accuracy of ghost-rotationally-invariant slave-boson theory for multiorbital Hubbard models and realistic materials
- Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning
- Derivation of the Ghost Gutzwiller Approximation from Quantum Embedding principles: the Ghost Density Matrix Embedding Theory
- Gaussian Process Regression Adaptive Density-Guided Approach: Towards Calculations of Potential Energy Surfaces for Larger Molecules
- Explicit inclusion of electronic correlation effects in molecular dynamics