Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning

Abstract

A cardinal obstacle to performing quantum-mechanical simulations of strongly-correlated matter is that, with the theoretical tools presently available, sufficiently-accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding (QE) methods with machine learning. This allows us to bypass altogether the most computationally-expensive components of QE algorithms, making their overall cost comparable to bare Density Functional Theory (DFT). We perform benchmark calculations of a series of actinide systems, where our method describes accurately the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry and materials science.

arXiv commentMain text (6 pages, 4 figures ) and supplemental material (5 pages)

References in corpus (41)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Electronic Structure Calculations with Dynamical Mean-Field Theory: A Spectral Density Functional Approach
• Perspective on density functional theory
• Quantum Cluster Theories
• By-passing the Kohn-Sham equations with machine learning
• Dynamical Mean-Field Theory within the Full-Potential Methods: Electronic structure of Ce-115 materials
• 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
• Nature of the 5f states in actinide metals
• Antiferromagnetism and d-wave superconductivity in cuprates: a uster DMFT study
• Variational cluster approach to correlated electron systems in low dimensions
• Quantum embedding theories
• Multi-band Gutzwiller wave functions for general on-site interactions
• Machine-learned multi-system surrogate models for materials prediction
• Rotationally-invariant slave-boson formalism and momentum dependence of the quasiparticle weight
• Variational matrix product state approach to quantum impurity models
• Machine learning for many-body physics: The case of the Anderson impurity model
• Density Matrix Embedding from Broken Symmetry Lattice Mean-Fields
• LDA+Gutzwiller Method for Correlated Electron Systems: Formalism and Its Applications
• Pure density functional for strong correlations and the thermodynamic limit from machine learning
• The Many-Body Expansion Combined with Neural Networks
• Efficient implementation of the Gutzwiller variational method
• Equivalence of Gutzwiller and slave-boson mean-field theories for multi-band Hubbard models
• Fermi-surface evolution across the magnetic phase transition in the Kondo lattice model
• Site-Selective Electronic Correlation in alpha-Plutonium Metal
• Slave Boson Theory of Orbital Differentiation with Crystal Field Effects: Application to UO${}_2$
• Spectral Properties of delta-Plutonium: Sensitivity to 5f Occupancy
• Quantum Monte Carlo calculations of structural properties of FeO under pressure
• Linear-scaling generation of potential energy surfaces using a double incremental expansion
• Magnetic Collapse and the Behavior of Transition Metal Oxides at High Pressure
• An dynamical-mean-field-theory investigation of specific heat and electronic structure of $\alpha$ and $\delta$-plutonium
• Landau-Gutzwiller quasi-particles
• First-principles Calculation of Atomic Forces and Structural Distortions in Strongly Correlated Materials
• Dynamical Mean Field Theory, Density-Matrix Embedding Theory and Rotationally Invariant Slave Bosons: a Unified Perspective
• Emergent Bloch Excitations in Mott Matter
• Correlated materials design: prospects and challenges
• Phase Stability and Properties of Manganese Oxide Polymorphs: Assessment and Insights from Diffusion Monte Carlo
• Rotationally invariant slave-boson and density matrix embedding theory: A unified framework and a comparative study on the 1D and 2D Hubbard Model
• Critical role of electronic correlations in determining crystal structure of transition metal compounds
• Dealing with the exponential wall in electronic structure calculations
• Mott transition in a metallic liquid - Gutzwiller molecular dynamics simulations

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• Linear Foundation Model for Quantum Embedding: Data-Driven Compression of the Ghost Gutzwiller Variational Space
• Efficient slave-boson approach for multiorbital two-particle response functions and superconductivity