Datadriven dynamical meanfield theory: An errorcorrection approach to solve the quantum manybody problem using machine learning
Abstract
Machine learning opens new avenues for modeling correlated materials. Quantum embedding approaches, such as dynamical meanfield theory (DMFT), provide corrections to firstprinciples calculations for strongly correlated materials, which are poorly described at lower levels of theory. Such embedding approaches are computationally demanding on classical computing architectures, and hence remain restricted to small systems, which limits the scope of applicability without exceptional computational resources. Here we outline a datadriven machinelearning process for solving the Anderson impurity model (AIM)—the central component of DMFT calculations. The key advance is the use of an ensemble errorcorrection approach to generate fast and accurate solutions of AIM. An example calculation of the Mott transition using DMFT in the single band Hubbard model is given as an example of the technique, and is validated against the most accurate available method. This approach is called datadriven dynamical meanfield theory (d^{3}MFT ) .
 Publication:

Physical Review B
 Pub Date:
 November 2021
 DOI:
 10.1103/PhysRevB.104.205120
 arXiv:
 arXiv:2107.13960
 Bibcode:
 2021PhRvB.104t5120S
 Keywords:

 Physics  Computational Physics;
 Condensed Matter  Strongly Correlated Electrons
 EPrint:
 doi:10.1103/PhysRevB.104.205120