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Title: Unfolding the prospects of computational (bio)materials modelling
Author: Sevink, Geert Jan Agur
Liwo, Adam
Asinari, Pietro
MacKernan, Donal
Milano, Giuseppe
Pagonabarraga Mora, Ignacio
Keywords: Dinàmica molecular
Aprenentatge automàtic
Molecular dynamics
Machine learning
Issue Date: 7-Sep-2020
Publisher: American Institute of Physics
Abstract: In 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.
Note: Reproducció del document publicat a:
It is part of: Journal of Chemical Physics, 2020, vol. 153, p. 100901
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ISSN: 0021-9606
Appears in Collections:Articles publicats en revistes (Física de la Matèria Condensada)

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