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Melting of a two-dimensional monodisperse cluster crystal to a cluster liquid
KTH, School of Engineering Sciences (SCI), Physics.
KTH, School of Engineering Sciences (SCI), Physics, Condensed Matter Theory.
KTH, School of Engineering Sciences (SCI), Applied Physics.ORCID iD: 0000-0003-1164-0831
KTH, School of Engineering Sciences (SCI), Applied Physics.ORCID iD: 0000-0002-9881-7857
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2019 (English)In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 99, no 4, article id 042140Article in journal (Refereed) Published
Abstract [en]

Monodisperse ensembles of particles that have cluster crystalline phases at low temperatures can model a number of physical systems, such as vortices in type-1.5 superconductors, colloidal suspensions, and cold atoms. In this work, we study a two-dimensional cluster-forming particle system interacting via an ultrasoft potential. We present a simple mean-field characterization of the cluster-crystal ground state, corroborating with Monte Carlo simulations for a wide range of densities. The efficiency of several Monte Carlo algorithms is compared, and the challenges of thermal equilibrium sampling are identified. We demonstrate that the liquid to cluster-crystal phase transition is of first order and occurs in a single step, and the liquid phase is a cluster liquid. © 2019 American Physical Society.

Place, publisher, year, edition, pages
American Physical Society , 2019. Vol. 99, no 4, article id 042140
Keywords [en]
Ground state, Intelligent systems, Liquids, Suspensions (fluids), Colloidal suspensions, Crystal phase transition, Crystalline phasis, Monodisperse clusters, Monte carlo algorithms, Particle systems, Thermal equilibriums, Two-dimensional clusters, Monte Carlo methods
National Category
Materials Chemistry
Identifiers
URN: urn:nbn:se:kth:diva-255902DOI: 10.1103/PhysRevE.99.042140Scopus ID: 2-s2.0-85064828694OAI: oai:DiVA.org:kth-255902DiVA, id: diva2:1344915
Note

Export Date: 24 May 2019; Article; Funding details: 621-2012-3984; Funding details: Vetenskapsrådet, 642-2013-7837; Funding text 1: W.W. and E.B. acknowledge support from the Swedish Research Council Grant No. 642-2013-7837 and the Goran Gustafsson Foundation for Research in Natural Sciences and Medicine. M.W. and R.D.M. acknowledge support from the Swedish Research Council Grant No. 621-2012-3984. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the National Supercomputer Centre (NSC) and the High Performance Computing Center North (HPC2N).

Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved

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Wang, WenlongWallin, MatsLidmar, JackBabaev, Egor

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