Deep convolutional neural networks for generating atomistic configurations of multi-component macromolecules from coarse-grained models.

Christofi, Eleftherios; Chazirakis, Antonis; Chrysostomou, Charalambos; Nicolaou, Mihalis A; Li, Wei; Doxastakis, Manolis; Harmandaris, Vagelis A

Christofi, Eleftherios; Chazirakis, Antonis; Chrysostomou, Charalambos; Nicolaou, Mihalis A; Li, Wei; Doxastakis, Manolis; Harmandaris, Vagelis A.

Affiliation

Christofi E; Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus.
Chazirakis A; Department of Mathematics and Applied Mathematics, University of Crete, Heraklion GR-71110, Greece.
Chrysostomou C; Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus.
Nicolaou MA; Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus.
Li W; Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo 001-0021, Japan.
Doxastakis M; Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.
Harmandaris VA; Computation-based Science and Technology Research Center, The Cyprus Institute, Nicosia 2121, Cyprus.

J Chem Phys ; 157(18): 184903, 2022 Nov 14.

Article de En | MEDLINE | ID: mdl-36379782

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Algorithmes / Type d'étude: Prognostic_studies Langue: En Journal: J Chem Phys Année: 2022 Type de document: Article Pays d'affiliation: Chypre

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