Molecule Structure Causal Modelling (SCM) of Choline Chloride Based Eutectic Solvents

This work applies the concept of structural causal modelling (SCM) for the predic - tion of eutectic temperatures of choline chloride based deep eutectic solvents (DES). Two SCM models were developed, one based on molecular descriptors (MD), and the other based on molecular fingerprints (MF). The models are presented in the form of directed acyclic graphs (DAG). The SCM-MD model shows that the chi simple cluster connectiv - ity descriptor (SC.5) and a number of hydrogen atoms (nH.1) are the key causal vari - ables. The causal relations between the model variables and eutectic temperature were determined after performing d -separation to block the variable confounding interference. The corresponding nonlinear causal relations were modelled by Bayes neural network with a single inner layer. Based on the SCM-MD model, a decision tree is proposed for the prediction of eutectic temperatures. Model performances were tested on a literature dataset of eutectic temperatures of ChCl based DESs. The SCM-MD model provided the most accurate prediction with an error of 7.5 °C.