Liquid synthetic fuels design guided by chemical structure: A machine learning perspective
Volume
39
Publisher
DOI
10.46855/energy-proceedings-10921
Metadata
Show full item recordAbstract
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.