Hoda Keshtkar - Department of Chemistry ,Shahrood University of Technology , Shahrood , Iran

A Group Contribution Method (GCM) combined with Support Vector Machine (SVM) algorithm was used to correlate and predict the speed of sound (SS) of ionic liquids at different pressures and tempressures. The several References Database were used to compile a dataset comprised of 114 ionic liquids and consisting of 849 experimental data values. The result is an 24-parameter model (GCM plus pressure and tempressure), which has the capability of prediction as well as correlation of the speed of sounds of ionic liquids. The proposed model has an average absolute relative deviation (AARD%) of 0.75%, a coefficient of determination (R2) of 0.9949, and a root mean square error (RSME) of 13.70 ms-1.

Speed of sound , Ionic liquids , Group contribution method , Support vector machine