Journal
FRONTIERS IN PHYSICS
Volume 6, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fphy.2018.00043
Keywords
downfolding; effective model; strongly correlated systems; quantum Monte Carlo; machine learning
Categories
Funding
- SciDAC [DOE FG02-12ER46875]
- NSF [DMR 1206242]
- Argonne Leadership Computing Facility, a U.S. Department of Energy, Office of Science User Facility [DE-AC02-06CH11357]
- U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering [DE-FG02-08ER46544]
- National Science Foundation [DGE-1144245, OCI-0725070, ACI-1238993]
- state of Illinois
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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).
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