440 citations · 3k across the 53 of their papers we have counts for
Showing 2016 · cond-mat.str-elShow all
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cond-mat.str-el2016★ 251 cited
Machine Learning Phases of Strongly Correlated Fermions
Kelvin Ch'ng, Juan Carrasquilla, Roger G. Melko +1
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to dist…
cond-mat.str-el2016★ 57 cited
Transport and Optical Conductivity in the Hubbard Model: A High-Temperature Expansion Perspective
Edward Perepelitsky, Andrew Galatas, Jernej Mravlje +4
We derive analytical expressions for the spectral moments of the dynamical response functions of the Hubbard model using the high-temperature series expansion. We consider generic…
cond-mat.str-el2016★ 26 cited
Three-dimensional Hubbard model in the thermodynamic limit
Ehsan Khatami
We employ the numerical linked-cluster expansion to study finite-temperature properties of the uniform cubic lattice Hubbard model in the thermodynamic limit for a wide range of in…