Mean-Field Caging in a Random Lorentz Gas.

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2021-06-07

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Abstract

The random Lorentz gas (RLG) is a minimal model of both percolation and glassiness, which leads to a paradox in the infinite-dimensional, d → ∞ limit: the localization transition is then expected to be continuous for the former and discontinuous for the latter. As a putative resolution, we have recently suggested that, as d increases, the behavior of the RLG converges to the glassy description and that percolation physics is recovered thanks to finite-d perturbative and nonperturbative (instantonic) corrections [Biroli et al. Phys. Rev. E 2021, 103, L030104]. Here, we expand on the d → ∞ physics by considering a simpler static solution as well as the dynamical solution of the RLG. Comparing the 1/d correction of this solution with numerical results reveals that even perturbative corrections fall out of reach of existing theoretical descriptions. Comparing the dynamical solution with the mode-coupling theory (MCT) results further reveals that, although key quantitative features of MCT are far off the mark, it does properly capture the discontinuous nature of the d → ∞ RLG. These insights help chart a path toward a complete description of finite-dimensional glasses.

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cond-mat.dis-nn, cond-mat.dis-nn, cond-mat.soft, cond-mat.stat-mech

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Published Version (Please cite this version)

10.1021/acs.jpcb.1c02067

Publication Info

Biroli, Giulio, Patrick Charbonneau, Yi Hu, Harukuni Ikeda, Grzegorz Szamel and Francesco Zamponi (2021). Mean-Field Caging in a Random Lorentz Gas. The journal of physical chemistry. B, 125(23). pp. 6244–6254. 10.1021/acs.jpcb.1c02067 Retrieved from https://hdl.handle.net/10161/24975.

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Scholars@Duke

Charbonneau

Patrick Charbonneau

Professor of Physics

Patrick Charbonneau is Professor of Physics at Duke University. His research in soft matter and statistical physics uses theory and computer simulations to study glassy materials and frustrated systems. He also contributes to the history of science, curating projects on quantum and statistical physics as well as food history.


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