Synergistic Modeling of Liquid Properties: Integrating Neural Network-Derived Molecular Features with Modified Kernel Models

A significant challenge in applying
machine learning
to computational
chemistry, particularly considering the growing complexity of contemporary
machine learning models, is the scarcity of available experimental
data. To address this issue, we introduce an approach that derives
molecular features from an intricate neural network-based model and
applies them to a simpler conventional machine learning model that
is robust to overfitting. This method can be applied to predict various
properties of a liquid system, including viscosity or surface tension,
based on molecular features drawn from the ab initio calculated free energy of solvation. Furthermore, we propose a modified
kernel model that includes Arrhenius temperature dependence to incorporate
theoretical principles and diminish extreme nonlinearity in the model.
The modified kernel model demonstrated significant improvements in
certain scenarios and possible extensions to various theoretical concepts
of molecular systems.