Unfolding the prospects of computational (bio)materials modelling

dc.contributor.authorSevink, Geert Jan Agur
dc.contributor.authorLiwo, Adam
dc.contributor.authorAsinari, Pietro
dc.contributor.authorMacKernan, Donal
dc.contributor.authorMilano, Giuseppe
dc.contributor.authorPagonabarraga Mora, Ignacio
dc.date.accessioned2021-02-03T11:05:38Z
dc.date.available2021-09-07T05:10:19Z
dc.date.issued2020-09-07
dc.date.updated2021-02-03T11:05:38Z
dc.description.abstractIn this perspective communication, we briefly sketch the current state of computational (bio)material research and discuss possible solutions for the four challenges that have been increasingly identified within this community: (i) the desire to develop a unified framework for testing the consistency of implementation and physical accuracy for newly developed methodologies, (ii) the selection of a standard format that can deal with the diversity of simulation data and at the same time simplifies data storage, data exchange, and data reproduction, (iii) how to deal with the generation, storage, and analysis of massive data, and (iv) the benefits of efficient 'core' engines. Expressed viewpoints are the result of discussions between computational stakeholders during a Lorentz center workshop with the prosaic title Workshop on Multi-scale Modeling and are aimed at (i) improving validation, reporting and reproducibility of computational results, (ii) improving data migration between simulation packages and with analysis tools, (iii) popularizing the use of coarse-grained and multi-scale computational tools among non-experts and opening up these modern computational developments to an extended user community.
dc.format.extent12 p.
dc.format.mimetypeapplication/pdf
dc.identifier.idgrec706722
dc.identifier.issn0021-9606
dc.identifier.urihttps://hdl.handle.net/2445/173616
dc.language.isoeng
dc.publisherAmerican Institute of Physics
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1063/5.0019773
dc.relation.ispartofJournal of Chemical Physics, 2020, vol. 153, p. 100901
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/676531/EU//E-CAM
dc.relation.urihttps://doi.org/10.1063/5.0019773
dc.rights(c) American Institute of Physics , 2020
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.sourceArticles publicats en revistes (Física de la Matèria Condensada)
dc.subject.classificationDinàmica molecular
dc.subject.classificationAprenentatge automàtic
dc.subject.otherMolecular dynamics
dc.subject.otherMachine learning
dc.titleUnfolding the prospects of computational (bio)materials modelling
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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