Abstract
Abstract Multiple linear regression (MLR) and artificial neural network (ANN) algorithms have been used to establish the quantitative structure-property relationship (QSPR) models for predicting the heat capacity of ionic liquids based on the S σ-profile descriptors calculated by COSMO-RS. To validate the two novel models, heat capacity data for 2416 experimental data points of 46 ILs was applied. The average absolute relative deviation (AARD) of test set of the MLR and ANN is 2.72 % and 0.64 %, respectively. Although the MLR and ANN can both be applied to predict the heat capacity, ANN algorithm has better performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
