activity
20162021
most citedLifshitz point at commensurate melting of 1D Rydberg atoms

31 citations · 69 across the 3 of their papers we have counts for

collaborators

11 papers

cond-mat.str-el2021

Supersymmetry and multicriticality in a ladder of constrained fermions

Natalia Chepiga, Jiří Minář, Kareljan Schoutens

Supersymmetric lattice models of constrained fermions are known to feature exotic phenomena such as superfrustration, with an extensive degeneracy of ground states, the nature of w…

cond-mat.str-el202131 cited

Lifshitz point at commensurate melting of 1D Rydberg atoms

Natalia Chepiga, Frédéric Mila

The recent investigation of chains of Rydberg atoms has brought back the problem of commensurate-incommensurate transitions into the focus of current research. In 2D classical syst…

cond-mat.str-el2020

Kibble-Zurek exponent and chiral transition of the period-4 phase of Rydberg chains

Natalia Chepiga, Frédéric Mila

Chains of Rydberg atoms have emerged as an amazing playground to study quantum physics in 1D. Playing with inter-atomic distances and laser detuning, one can in particular explore…

cond-mat.str-el2019

Ground-state properties of the hydrogen chain: insulator-to-metal transition, dimerization, and magnetic phases

Mario Motta, Claudio Genovese, Fengjie Ma +14

Accurate and predictive computations of the quantum-mechanical behavior of many interacting electrons in realistic atomic environments are critical for the theoretical design of ma…

cond-mat.str-el2019

Dimerization and effective decoupling in two spin-1 generalizations of the spin-1/2 Majumdar-Ghosh chain

Natalia Chepiga, Frédéric Mila

We perform a systematic DMRG investigation of the two natural spin-1 generalizations of the spin-1/2 Majumdar-Ghosh chain, the spin-1 Heisenberg chain, where is a n…

cond-mat.str-el201928 cited

Comb tensor networks

Natalia Chepiga, Steven R. White

In this paper we propose a special type of a tree tensor network that has the geometry of a comb---a 1D backbone with finite 1D teeth projecting out from it. This tensor network is…