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Autor principal: Faggionato, Alessandra
Formato: Preprint
Publicado: 2023
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Acceso en línea:https://arxiv.org/abs/2301.06318
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author Faggionato, Alessandra
author_facet Faggionato, Alessandra
contents Mott's variable range hopping (v.r.h.) is the phonon-induced hopping of electrons in disordered solids (such as doped semiconductors) within the regime of strong Anderson localization. It was introduced by N.~Mott to explain the anomalous low temperature conductivity decay in dimension $d\geq 2$, corresponding now to the so called Mott's law. We provide a rigorous derivation of this Physics law for two effective models of Mott v.r.h.: the random resistor network for v.r.h. of \cite[Section~IV]{AHL} and Mott's random walk. We also determine the constant multiplying the power of the inverse temperature in the exponent in Mott's law, which was an open problem also on a heuristic level.
format Preprint
id arxiv_https___arxiv_org_abs_2301_06318
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Mott's law for the v.r.h. random resistor network and for Mott's random walk
Faggionato, Alessandra
Probability
Disordered Systems and Neural Networks
Statistical Mechanics
Mathematical Physics
60G55, 82B43, 82D30
Mott's variable range hopping (v.r.h.) is the phonon-induced hopping of electrons in disordered solids (such as doped semiconductors) within the regime of strong Anderson localization. It was introduced by N.~Mott to explain the anomalous low temperature conductivity decay in dimension $d\geq 2$, corresponding now to the so called Mott's law. We provide a rigorous derivation of this Physics law for two effective models of Mott v.r.h.: the random resistor network for v.r.h. of \cite[Section~IV]{AHL} and Mott's random walk. We also determine the constant multiplying the power of the inverse temperature in the exponent in Mott's law, which was an open problem also on a heuristic level.
title Mott's law for the v.r.h. random resistor network and for Mott's random walk
topic Probability
Disordered Systems and Neural Networks
Statistical Mechanics
Mathematical Physics
60G55, 82B43, 82D30
url https://arxiv.org/abs/2301.06318