Machine Learning of Molecular Classification and Quantum Mechanical Calculations

In this paper, a machine learning method is proposed to extract molecular features as floating-point numbers in a high dimensional space from the language-like description Simplified Molecular Input Line Entry Specification (SMILES). Principle component analysis showed that this method can successfully classify alkanes and alcohols and also the chain lengths of the molecular by their location in a three-dimensional feature space. A neural network model is build using the location of a compound in this high dimensional space as input to predict the “sigma-profile”, the charge distribution of the molecule near a perfect infinite conductor, which is calculated by quantum mechanics. The sigma-profile can be used in the COSMOSAC model for predicting thermodynamic properties such as activity coefficient. Preliminary results showed that an accurate neural work model with generalization ability can be developed.