No Cover Image

Journal article 176 views 41 downloads

Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids

Daniel York Orcid Logo, Isaac Vidal-Daza, Cristina Segura, Jose Norambuena-Contreras Orcid Logo, Francisco Martin-Martinez

Digital Discovery, Volume: 3, Issue: 6, Pages: 1108 - 1122

Swansea University Authors: Daniel York Orcid Logo, Jose Norambuena-Contreras Orcid Logo, Francisco Martin-Martinez

  • 66761.vor.pdf

    PDF | Version of Record

    Distributed under the terms of a Creative Commons CC-BY Attribution licence.

    Download (2.3MB)

Check full text

DOI (Published version): 10.1039/d3dd00245d

Abstract

Complex molecular organic fluids such as bitumen, lubricants, crude oil, or biobased oils from biorefineries are intrinsically challenging to model with molecular precision, given the large variety and complexity of organic molecules in their composition. Large scale atomistic simulations have been...

Full description

Published in: Digital Discovery
ISSN: 2635-098X
Published: Royal Society of Chemistry (RSC) 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa66761
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2024-06-20T08:33:11Z
last_indexed 2024-06-20T08:33:11Z
id cronfa66761
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>66761</id><entry>2024-06-20</entry><title>Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids</title><swanseaauthors><author><sid>2fa8c0fb8b8b0faef2beada0f8ec54e4</sid><ORCID>0000-0002-4038-9239</ORCID><firstname>Daniel</firstname><surname>York</surname><name>Daniel York</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>73c6854ebb10465fbf7faab297135641</sid><ORCID>0000-0001-8327-2236</ORCID><firstname>Jose</firstname><surname>Norambuena-Contreras</surname><name>Jose Norambuena-Contreras</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>a5907aac618ec107662c888f6ead0e4a</sid><firstname>Francisco</firstname><surname>Martin-Martinez</surname><name>Francisco Martin-Martinez</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-06-20</date><deptcode>EAAS</deptcode><abstract>Complex molecular organic fluids such as bitumen, lubricants, crude oil, or biobased oils from biorefineries are intrinsically challenging to model with molecular precision, given the large variety and complexity of organic molecules in their composition. Large scale atomistic simulations have been historically limited by this complexity, which has hampered the bottom-up molecular design of these materials, something especially relevant given the current surge of biobased fluids for sustainable applications and the cost of trial-and-error experimental developments. To address this limitation, we have developed an author-agnostic computational framework to generate data-driven representative models of any complex mixture of organic molecules directly from Gas Chromatography-Mass Spectrometry (GCMS) experimental characterisation, thus reducing human biases in model creation and providing a platform for self-driven digital development of molecular organic fluids. The method proposed generates statistically representative molecular samples that simplify the complexity of the fluid in a limited group of molecules, while capturing the critical chemical features needed to describe the overall properties of the mixture. As a case study, we generated a showcase of data-driven representative models from the GCMS characterisation of a bio-oil from the pyrolysis of pine bark, specially produced for this study. Pyrolytic biomass processing into bio-oils provides a waste valorisation route with applications in biorefinery products like asphalt additives and biofuel precursors. Our case study focuses on complex fluids such as bio-oils for asphalt rejuvenators for self-healing purposes or biofuel upgrading. Nevertheless, the general computational framework developed in this manuscript provides a platform for generating data-driven representative models of any bitumen or biobased organic fluid.</abstract><type>Journal Article</type><journal>Digital Discovery</journal><volume>3</volume><journalNumber>6</journalNumber><paginationStart>1108</paginationStart><paginationEnd>1122</paginationEnd><publisher>Royal Society of Chemistry (RSC)</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2635-098X</issnElectronic><keywords/><publishedDay>16</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-04-16</publishedDate><doi>10.1039/d3dd00245d</doi><url/><notes/><college>COLLEGE NANME</college><department>Engineering and Applied Sciences School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>EAAS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>This work was supported by the Royal Society of Chemistry Enablement Grant (E21-7051491439), the Engineering and Physical Sciences Research Council's Industrial CASE ref. 220197 with Tata Steel, and the Chilean Economic Development Agency (CORFO) through the R&amp;D project Prototype and Validation (grant number 19CVID-107445). The authors also acknowledge the support of the Supercomputing Wales project which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government, and the support of the Google Cloud Platform Credits program with the award GCP19980904. F. J. M.-M. acknowledges the support from the Google Cloud Research Innovators program.</funders><projectreference/><lastEdited>2024-10-29T11:18:17.2508836</lastEdited><Created>2024-06-20T09:30:27.4382289</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering</level></path><authors><author><firstname>Daniel</firstname><surname>York</surname><orcid>0000-0002-4038-9239</orcid><order>1</order></author><author><firstname>Isaac</firstname><surname>Vidal-Daza</surname><order>2</order></author><author><firstname>Cristina</firstname><surname>Segura</surname><order>3</order></author><author><firstname>Jose</firstname><surname>Norambuena-Contreras</surname><orcid>0000-0001-8327-2236</orcid><order>4</order></author><author><firstname>Francisco</firstname><surname>Martin-Martinez</surname><order>5</order></author></authors><documents><document><filename>66761__30922__27f74d2755d5461da4e2ad1affac5d8b.pdf</filename><originalFilename>66761.vor.pdf</originalFilename><uploaded>2024-07-17T14:14:44.5430963</uploaded><type>Output</type><contentLength>2407607</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Distributed under the terms of a Creative Commons CC-BY Attribution licence.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/3.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 66761 2024-06-20 Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids 2fa8c0fb8b8b0faef2beada0f8ec54e4 0000-0002-4038-9239 Daniel York Daniel York true false 73c6854ebb10465fbf7faab297135641 0000-0001-8327-2236 Jose Norambuena-Contreras Jose Norambuena-Contreras true false a5907aac618ec107662c888f6ead0e4a Francisco Martin-Martinez Francisco Martin-Martinez true false 2024-06-20 EAAS Complex molecular organic fluids such as bitumen, lubricants, crude oil, or biobased oils from biorefineries are intrinsically challenging to model with molecular precision, given the large variety and complexity of organic molecules in their composition. Large scale atomistic simulations have been historically limited by this complexity, which has hampered the bottom-up molecular design of these materials, something especially relevant given the current surge of biobased fluids for sustainable applications and the cost of trial-and-error experimental developments. To address this limitation, we have developed an author-agnostic computational framework to generate data-driven representative models of any complex mixture of organic molecules directly from Gas Chromatography-Mass Spectrometry (GCMS) experimental characterisation, thus reducing human biases in model creation and providing a platform for self-driven digital development of molecular organic fluids. The method proposed generates statistically representative molecular samples that simplify the complexity of the fluid in a limited group of molecules, while capturing the critical chemical features needed to describe the overall properties of the mixture. As a case study, we generated a showcase of data-driven representative models from the GCMS characterisation of a bio-oil from the pyrolysis of pine bark, specially produced for this study. Pyrolytic biomass processing into bio-oils provides a waste valorisation route with applications in biorefinery products like asphalt additives and biofuel precursors. Our case study focuses on complex fluids such as bio-oils for asphalt rejuvenators for self-healing purposes or biofuel upgrading. Nevertheless, the general computational framework developed in this manuscript provides a platform for generating data-driven representative models of any bitumen or biobased organic fluid. Journal Article Digital Discovery 3 6 1108 1122 Royal Society of Chemistry (RSC) 2635-098X 16 4 2024 2024-04-16 10.1039/d3dd00245d COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University Another institution paid the OA fee This work was supported by the Royal Society of Chemistry Enablement Grant (E21-7051491439), the Engineering and Physical Sciences Research Council's Industrial CASE ref. 220197 with Tata Steel, and the Chilean Economic Development Agency (CORFO) through the R&D project Prototype and Validation (grant number 19CVID-107445). The authors also acknowledge the support of the Supercomputing Wales project which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government, and the support of the Google Cloud Platform Credits program with the award GCP19980904. F. J. M.-M. acknowledges the support from the Google Cloud Research Innovators program. 2024-10-29T11:18:17.2508836 2024-06-20T09:30:27.4382289 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Daniel York 0000-0002-4038-9239 1 Isaac Vidal-Daza 2 Cristina Segura 3 Jose Norambuena-Contreras 0000-0001-8327-2236 4 Francisco Martin-Martinez 5 66761__30922__27f74d2755d5461da4e2ad1affac5d8b.pdf 66761.vor.pdf 2024-07-17T14:14:44.5430963 Output 2407607 application/pdf Version of Record true Distributed under the terms of a Creative Commons CC-BY Attribution licence. true eng http://creativecommons.org/licenses/by/3.0/
title Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids
spellingShingle Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids
Daniel York
Jose Norambuena-Contreras
Francisco Martin-Martinez
title_short Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids
title_full Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids
title_fullStr Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids
title_full_unstemmed Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids
title_sort Data-driven representative models to accelerate scaled-up atomistic simulations of bitumen and biobased complex fluids
author_id_str_mv 2fa8c0fb8b8b0faef2beada0f8ec54e4
73c6854ebb10465fbf7faab297135641
a5907aac618ec107662c888f6ead0e4a
author_id_fullname_str_mv 2fa8c0fb8b8b0faef2beada0f8ec54e4_***_Daniel York
73c6854ebb10465fbf7faab297135641_***_Jose Norambuena-Contreras
a5907aac618ec107662c888f6ead0e4a_***_Francisco Martin-Martinez
author Daniel York
Jose Norambuena-Contreras
Francisco Martin-Martinez
author2 Daniel York
Isaac Vidal-Daza
Cristina Segura
Jose Norambuena-Contreras
Francisco Martin-Martinez
format Journal article
container_title Digital Discovery
container_volume 3
container_issue 6
container_start_page 1108
publishDate 2024
institution Swansea University
issn 2635-098X
doi_str_mv 10.1039/d3dd00245d
publisher Royal Society of Chemistry (RSC)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description Complex molecular organic fluids such as bitumen, lubricants, crude oil, or biobased oils from biorefineries are intrinsically challenging to model with molecular precision, given the large variety and complexity of organic molecules in their composition. Large scale atomistic simulations have been historically limited by this complexity, which has hampered the bottom-up molecular design of these materials, something especially relevant given the current surge of biobased fluids for sustainable applications and the cost of trial-and-error experimental developments. To address this limitation, we have developed an author-agnostic computational framework to generate data-driven representative models of any complex mixture of organic molecules directly from Gas Chromatography-Mass Spectrometry (GCMS) experimental characterisation, thus reducing human biases in model creation and providing a platform for self-driven digital development of molecular organic fluids. The method proposed generates statistically representative molecular samples that simplify the complexity of the fluid in a limited group of molecules, while capturing the critical chemical features needed to describe the overall properties of the mixture. As a case study, we generated a showcase of data-driven representative models from the GCMS characterisation of a bio-oil from the pyrolysis of pine bark, specially produced for this study. Pyrolytic biomass processing into bio-oils provides a waste valorisation route with applications in biorefinery products like asphalt additives and biofuel precursors. Our case study focuses on complex fluids such as bio-oils for asphalt rejuvenators for self-healing purposes or biofuel upgrading. Nevertheless, the general computational framework developed in this manuscript provides a platform for generating data-driven representative models of any bitumen or biobased organic fluid.
published_date 2024-04-16T11:18:15Z
_version_ 1814246924983730176
score 11.035655

Similar Items