eprintid: 10041856 rev_number: 27 eprint_status: archive userid: 608 dir: disk0/10/04/18/56 datestamp: 2018-01-26 16:54:13 lastmod: 2021-09-20 22:19:39 status_changed: 2018-01-26 16:54:13 type: article metadata_visibility: show creators_name: Zhang, X creators_name: Ding, B creators_name: Cheng, R creators_name: Dixon, SC creators_name: Lu, Y title: Computational Intelligence-Assisted Understanding of Nature-Inspired Superhydrophobic Behavior ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: C06 divisions: F56 keywords: artificial neural networks; computational intelligence; evolutionary computation; superhydrophobic behavior note: Copyright © 2017 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. abstract: In recent years, state-of-the-art computational modeling of physical and chemical systems has shown itself to be an invaluable resource in the prediction of the properties and behavior of functional materials. However, construction of a useful computational model for novel systems in both academic and industrial contexts often requires a great depth of physicochemical theory and/or a wealth of empirical data, and a shortage in the availability of either frustrates the modeling process. In this work, computational intelligence is instead used, including artificial neural networks and evolutionary computation, to enhance our understanding of nature-inspired superhydrophobic behavior. The relationships between experimental parameters (water droplet volume, weight percentage of nanoparticles used in the synthesis of the polymer composite, and distance separating the superhydrophobic surface and the pendant water droplet in adhesive force measurements) and multiple objectives (water droplet contact angle, sliding angle, and adhesive force) are built and weighted. The obtained optimal parameters are consistent with the experimental observations. This new approach to materials modeling has great potential to be applied more generally to aid design, fabrication, and optimization for myriad functional materials. date: 2018-01 date_type: published official_url: http://dx.doi.org/10.1002/advs.201700520 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green article_type_text: Article verified: verified_manual elements_id: 1520753 doi: 10.1002/advs.201700520 lyricists_name: Dixon, Sebastian lyricists_name: Lu, Yao lyricists_name: Zhang, Xia lyricists_id: SDIXO04 lyricists_id: LUBXX92 lyricists_id: XZHAE61 actors_name: Dewerpe, Marie actors_id: MDDEW97 actors_role: owner full_text_status: public publication: Advanced Science volume: 5 number: 1 article_number: 1700520 issn: 2198-3844 citation: Zhang, X; Ding, B; Cheng, R; Dixon, SC; Lu, Y; (2018) Computational Intelligence-Assisted Understanding of Nature-Inspired Superhydrophobic Behavior. Advanced Science , 5 (1) , Article 1700520. 10.1002/advs.201700520 <https://doi.org/10.1002/advs.201700520>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10041856/1/Zhang_et_al-2018-Advanced_Science.pdf document_url: https://discovery.ucl.ac.uk/id/eprint/10041856/8/Zhang_Computational_Intelligence-Assisted%20_Understanding_Suppl.pdf