| dc.contributor.author | Freitas, RSM | |
| dc.contributor.author | Chen, C | |
| dc.contributor.author | Jiang, X | |
| dc.date.accessioned | 2024-05-24T15:05:36Z | |
| dc.date.available | 2024-05-24T15:05:36Z | |
| dc.date.issued | 2024-01-01 | |
| dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/97048 | |
| dc.description.abstract | Physicochemical properties of synthetic fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning (ML) models are constructed to discover intrinsic chemical structure-properties relationships. The models are trained using data from molecular dynamics (MD) simulations. The fuel structure is represented by molecular descriptors. Such a symbolic representation of the fuel molecule allows to link important features of the fuel composition with key properties of fuel utilization. The results show that the present approach can predict accurately the fuel properties of a wide range of pressure and temperature conditions. | en_US |
| dc.publisher | Applied Energy Innovation Institute (AEii) | en_US |
| dc.rights | This item is distributed under the terms of the Creative Commons Attribution 4.0 Unported License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. | |
| dc.title | Liquid synthetic fuels design guided by chemical structure: A machine learning perspective | en_US |
| dc.type | Conference Proceeding | en_US |
| dc.rights.holder | © 2023, The Author(s). | |
| dc.identifier.doi | 10.46855/energy-proceedings-10921 | |
| pubs.notes | Not known | en_US |
| pubs.publication-status | Published | en_US |
| pubs.volume | 39 | en_US |
| rioxxterms.funder | Default funder | en_US |
| rioxxterms.identifier.project | Default project | en_US |
| qmul.funder | Utilisation of Synthetic Fuels for "Difficult-to-Decarbonise" Propulsion::Engineering and Physical Sciences Research Council | en_US |
