Direct coal liquefaction (DCL) is an important and effective method of converting coal into high-value-added chemicals and fuel oil. In DCL

heating the direct coal liquefaction solvent (DCLS) from low to high temperature and pre-hydrogenation of the DCLS are critical steps. Therefore

studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance. However

it is difficult to precisely determine hydrogen solubility only by experiments

especially under the actual DCL conditions. To address this issue

we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship (ML-QSPR) methods. The results showed that the squared correlation coefficient

R

2

= 0.92 and root mean square error RMSE = 0.095

indicating the model’s good statistical performance. The external validation of the model also reveals excellent accuracy and predictive ability. Molecular polarization (

α

) is the main factor affecting the dissolution of hydrogen in DCLS. The hydrogen solubility in acyclic alkanes increases with increasing carbon number. Whereas in polycyclic aromatics

it decreases wi

th increasing ring number

and in hydrogenated aromatics

it increases with hydrogenation degree. This work provides a new reference for the selection and proportioning of DCLS

i.e.

a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure. Besides

it brings important insight into the theoretical significance and practical value of the DCL.