Prediction of acetylene solubility by a mechanism-data hybrid-driven machine learning model constructed based on COSMO-RS theory

Abstract

In this work, a mechanism-data hybrid-driven machine learning model is built to predict acetylene solubility from COSMO-derived molecular descriptors, overcoming the challenge of limited training samples. We have successfully enhanced the data-learning capabilities of the model by mechanism-level modelling, and generated new mechanism-level understanding from the results of data learning. On the basis of the mechanism-level understanding of molecular interactions, this model gives more accurate predictions than the baseline models, even though it contains only 37 model parameters. Meanwhile, it presents better generalization to predict solubility in solvents beyond the training sample. Furthermore, by analyzing the implication of the parameters of this model, we find a charged segment interaction energy form different from that described by the classic COSMO-RS theory. This work not only produces available acetylene solubility data, but also presents a proposed framework to build a mechanism-data hybrid-driven model for thermodynamic properties and discovering interpretable chemical laws from small training samples.