From Real Materials to Model Hamiltonians With Density Matrix Downfolding

Frontiers in Physics

Zheng, Huihuo¹  Changlani, Hitesh J.²  Williams, Kiel T.³  Busemeyer, Brian³  Wagner, Lucas K.³ 

[1] Argonne Leadership Computing Facility, Argonne National Laboratory, United States;Department of Physics and Astronomy and Institute for Quantum Matter, Johns Hopkins University, United States;Department of Physics and Institute for Condensed Matter Theory, University of Illinois at Urbana-Champaign, United States

关键词: Downfolding;    Effective model;    Strongly Correlated Systems;    quantum Monte Carlo;    machine learning;   

DOI  :  10.3389/fphy.2018.00043

学科分类：物理（综合）

来源: Frontiers

【 摘 要 】

Due to advances in computer hardware and new algorithms, it is now possible to perform highly accurate many-body simulations of realistic materials with all their intrinsic complications. The success of these simulations leaves us with a conundrum: how do we extract useful physical models and insight from these simulations? In this article, we present a formal theory of downfolding–extracting an effective Hamiltonian from first-principles calculations. The theory maps the downfolding problem into fitting information derived from wave functions sampled from a low-energy subspace of the full Hilbert space. Since this fitting process most commonly uses reduced density matrices, we term it density matrix downfolding (DMD).

【 授权许可】

CC BY