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dc.contributor.authorBeardsley, T.M.
dc.contributor.authorSpencer, R.K.W.
dc.contributor.authorMatsen, M.W.
dc.date.accessioned2019-12-06 23:07:39 (GMT)
dc.date.available2019-12-06 23:07:39 (GMT)
dc.date.issued2019-11-12
dc.identifier.urihttps://doi.org/10.1021/acs.macromol.9b01904
dc.identifier.urihttp://hdl.handle.net/10012/15292
dc.description.abstractField-theoretic simulations (FTS) provide fluctuation corrections to self-consistent field theory (SCFT) by simulating its field-theoretic Hamiltonian rather than applying the saddle-point approximation. Although FTS work well for ultrahigh molecular weights, they have struggled with experimentally relevant values. Here, we consider FTS for two-component (i.e., AB-type) melts, where the composition field fluctuates but the saddle-point approximation is still applied to the pressure field that enforces incompressibility. This results in real-valued fields, thereby allowing for conventional simulation methods. We discover that Langevin simulations are one to two orders of magnitude faster than previous Monte Carlo simulations, which permits us to accurately calculate the order-disorder transition of symmetric diblock copolymer melts at realistic molecular weights. This remarkable speedup will, likewise, facilitate FTS for more complicated block copolymer systems, which might otherwise be unfeasible with traditional particle-based simulations.en
dc.description.sponsorshipNational Science and Engineering Research Council (NSERC) of Canada || Compute Canadaen
dc.language.isoenen
dc.publisherAmerican Chemical Societyen
dc.titleComputationally Efficient Field-Theoretic Simulations for Block Copolymer Meltsen
dc.typeArticleen
dcterms.bibliographicCitationT. M. Beardsley, R. K. W. Spencer and M. W. Matsen, Macromolecules 52, 8840 (2019)en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation1Faculty of Scienceen
uws.contributor.affiliation2Chemical Engineeringen
uws.contributor.affiliation2Physics and Astronomyen
uws.contributor.affiliation2Waterloo Institute for Nanotechnology (WIN)en
uws.typeOfResourceTexten
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen
uws.scholarLevelPost-Doctorateen


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