Abstract
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).
Original language | English (US) |
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Article number | 43 |
Journal | Frontiers in Physics |
Volume | 6 |
Issue number | MAY |
DOIs | |
State | Published - May 11 2018 |
Keywords
- Downfolding
- Effective model
- Machine learning
- Quantum Monte Carlo
- Strongly correlated systems
ASJC Scopus subject areas
- Biophysics
- Materials Science (miscellaneous)
- Mathematical Physics
- General Physics and Astronomy
- Physical and Theoretical Chemistry