Machine learning approach to map the thermal conductivity of over 2,000 neoteric solvents for green energy storage applications

Lemaoui, T; Darwish, AS; Almustafa, G; Boublia, A; Sarika, PR; Jabbar, NA; Ibrahim, T; Nancarrow, P; Yadav, KK; Fallatah, AM; Abbas, M; Algethami, JS; Benguerba, Y; Jeon, BH; Banat, F; AlNashef, IM

Machine learning approach to map the thermal conductivity of over 2,000 neoteric solvents for green energy storage applications

Lemaoui, T Darwish, AS Almustafa, G

Boublia, A Sarika, PR Jabbar, NA Ibrahim, T Nancarrow, P Yadav, KK Fallatah, AM Abbas, M Algethami, JS Benguerba, Y Jeon, BH Banat, F AlNashef, IM

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Energy Storage Materials, 2023, 59
Issue Date:: 2023-05-01

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Field	Value	Language
dc.contributor.author	Lemaoui, T
dc.contributor.author	Darwish, AS
dc.contributor.author	Almustafa, G https://orcid.org/0000-0003-1251-9945
dc.contributor.author	Boublia, A
dc.contributor.author	Sarika, PR
dc.contributor.author	Jabbar, NA
dc.contributor.author	Ibrahim, T
dc.contributor.author	Nancarrow, P
dc.contributor.author	Yadav, KK
dc.contributor.author	Fallatah, AM
dc.contributor.author	Abbas, M
dc.contributor.author	Algethami, JS
dc.contributor.author	Benguerba, Y
dc.contributor.author	Jeon, BH
dc.contributor.author	Banat, F
dc.contributor.author	AlNashef, IM
dc.date.accessioned	2024-04-23T02:43:28Z
dc.date.available	2024-04-23T02:43:28Z
dc.date.issued	2023-05-01
dc.identifier.citation	Energy Storage Materials, 2023, 59
dc.identifier.issn	2405-8297
dc.identifier.issn	2405-8297
dc.identifier.uri	http://hdl.handle.net/10453/178261
dc.description.abstract	Interest in green neoteric solvents, such as ionic liquids (ILs) and deep eutectic solvents (DESs), has increased dramatically in recent years due to their highly tunable properties. One application that has stimulated many experimental studies is their use as green solvents in energy and heat storage. Nevertheless, their theoretically infinite chemical space hinders their practical application and makes it impossible to conclude universal laws regarding their feasibility. Herein, for the first time, we combine molecular modeling and machine learning (ML) to develop a holistic tool that can map the thermal conductivity space of both ILs and DESs to bring their use as green solvents into industrial reality. Two molecular representations were used: the σ-profiles (σp) and the critical properties (CPs). In addition, six ML algorithms were evaluated, and the results showed that artificial neural networks (ANNs) demonstrated fast and accurate predictions of the thermal conductivity space with R2 values of 0.995 and 0.991 using σp and CPs, respectively. The ANNs were further experimentally validated by additional measurements of 5 ILs and 5 DESs, which have not been previously reported in the literature. The results showed an excellent agreement, with deviations of only 2.82% and 2.71% using σp and CPs, respectively. Subsequently, the ANNs were used to successfully screen 1,156 ILs and 1,125 DESs to demonstrate a guided molecular design to achieve different thermal conductivity values. The proposed ANNs were also loaded into an easy-to-use spreadsheet included in the Supplementary materials. This work showcases the power of data-centric modeling for predicting the chemical spaces of ILs and DESs to promote their use as green solvents for various potential applications, including energy storage, fuel cells, and carbon dioxide capture.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Energy Storage Materials
dc.relation.isbasedon	10.1016/j.ensm.2023.102795
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0904 Chemical Engineering, 0906 Electrical and Electronic Engineering
dc.title	Machine learning approach to map the thermal conductivity of over 2,000 neoteric solvents for green energy storage applications
dc.type	Journal Article
utslib.citation.volume	59
utslib.for	0904 Chemical Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-23T02:43:26Z
pubs.publication-status	Published
pubs.volume	59

Abstract:

Interest in green neoteric solvents, such as ionic liquids (ILs) and deep eutectic solvents (DESs), has increased dramatically in recent years due to their highly tunable properties. One application that has stimulated many experimental studies is their use as green solvents in energy and heat storage. Nevertheless, their theoretically infinite chemical space hinders their practical application and makes it impossible to conclude universal laws regarding their feasibility. Herein, for the first time, we combine molecular modeling and machine learning (ML) to develop a holistic tool that can map the thermal conductivity space of both ILs and DESs to bring their use as green solvents into industrial reality. Two molecular representations were used: the σ-profiles (σp) and the critical properties (CPs). In addition, six ML algorithms were evaluated, and the results showed that artificial neural networks (ANNs) demonstrated fast and accurate predictions of the thermal conductivity space with R2 values of 0.995 and 0.991 using σp and CPs, respectively. The ANNs were further experimentally validated by additional measurements of 5 ILs and 5 DESs, which have not been previously reported in the literature. The results showed an excellent agreement, with deviations of only 2.82% and 2.71% using σp and CPs, respectively. Subsequently, the ANNs were used to successfully screen 1,156 ILs and 1,125 DESs to demonstrate a guided molecular design to achieve different thermal conductivity values. The proposed ANNs were also loaded into an easy-to-use spreadsheet included in the Supplementary materials. This work showcases the power of data-centric modeling for predicting the chemical spaces of ILs and DESs to promote their use as green solvents for various potential applications, including energy storage, fuel cells, and carbon dioxide capture.

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http://hdl.handle.net/10453/178261