Direct coal liquefaction products offer a considerable quantity of cycloalkanes

which are the valuable candidates for making the high energy density fuels. The creation of such fuels depends on designing molecular structures and calculating their properties

which can be expedited with computer-aided techniques. In this study

a dataset containing 367 fuel molecules was constructed based on the analysis of direct coal liquefied oil. Three convolutional neural network property prediction models have been created based on molecular structure-physical and chemical property data from the library. All the models have good fitting ability with

R

2

values above 0.97. Then

a variational autoencoder generation model has been established using the molecular structures from the library

focusing on the structure of saturated cycloalkanes. The structure-property prediction model was then applied to the newly generated molecules

assessing their density

volumetric calorific value

and melting point. As a result

70000 novel molecular structures were generated

and 25 molecular structures meeting the criteria for high energy density fuels were identified. The established variational autoencoder model in this study effectively assimilates the str

uctural information from the sample set and autonomously generates novel high energy density fuels

which is difficult to achieve in traditional experimental methods.