Novel molecular descriptors for prediction of H2S solubility in ionic liquids

Molecular descriptors are very important input parameters for establishing properties prediction models of materials, such as ionic liquids (ILs). In this work, as a new class of molecular descriptors, namely, electrostatic potential surface (SEP) is proposed to predict one of the important representative properties of ILs, i.e. the H2S solubility in ILs. 1318 experimental data points of 28 ILs, including 7 cations and 12 anions covering diverse temperatures and pressures, have been gathered from 15 references. According to the qualitative analyses, it is found that anions play a more important role than cations for the H2S solubility in ILs, besides the anions with stronger hydrogen-bond basicity have higher capacities to absorb H2S. Combining the SEP descriptors with the extreme learning machine (ELM) algorithm, two new quantitative models (ELM1 based on the isolated ions and ELM2 based on the ion pairs) for predicting H2S solubility are established. The average absolute relative deviation (AARD%) for the total set of ELM1 and ELM2 models are 5.87% and 3.84%, respectively. The results indicate that the SEP descriptors can extensively be employed to predict properties of ILs due to their rich information at electron level.