2018
DOI: 10.3389/fphy.2018.00043
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From Real Materials to Model Hamiltonians With Density Matrix Downfolding

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 infor… Show more

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Cited by 29 publications
(24 citation statements)
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“…where ðr 1 ; …; r N Þ are the electron coordinates, and R a ¼ aRe z is the coordinate of the ath proton. This Hamiltonian has been studied in the finite basis or finite systems (or both) and has generated increasing interest [8,10,11,[13][14][15][16]. We use atomic units throughout, in which energies are measured in Hartrees (me 4 =ℏ 2 ) and lengths in units of the Bohr radius a B ¼ ℏ 2 =ðme 2 Þ.…”
Section: Methodsmentioning
confidence: 99%
“…where ðr 1 ; …; r N Þ are the electron coordinates, and R a ¼ aRe z is the coordinate of the ath proton. This Hamiltonian has been studied in the finite basis or finite systems (or both) and has generated increasing interest [8,10,11,[13][14][15][16]. We use atomic units throughout, in which energies are measured in Hartrees (me 4 =ℏ 2 ) and lengths in units of the Bohr radius a B ¼ ℏ 2 =ðme 2 Þ.…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, the singlet-triplet excitation is J ¼ 492ð24Þ meV, within two statistical error bars of the experimental result. This realization was made possible by work we have done regarding effective model fitting [3,4]. We view this as a strong motivation to have a rigorous theory of effective models as we tried to lay out in those papers.…”
mentioning
confidence: 99%
“…To overcome these limitations, we developed a novel inverse method that automates the construction of parent Hamiltonians from wave functions by searching for models in a large space of "physically reasonable" Hamiltonians. More broadly, inverse methods have been successful in applications such as solving machine learning problems [25], targeting many-particle ordering in classical materials [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41], and promoting certain properties in quantum many-body systems [42][43][44][45][46][47].…”
Section: Introductionmentioning
confidence: 99%