Pure Component Property Estimation Framework Using Explainable Machine Learning Methods

TL;DR

This paper introduces an explainable machine learning framework for predicting pure component properties, utilizing molecular connectivity features, feature ranking, and interpretability techniques to improve accuracy and understanding.

Contribution

The work presents a novel framework combining molecular connectivity representations, feature pooling, and Shapley value analysis for accurate and interpretable property prediction.

Findings

Root-mean-square error reduced by up to 83.8% compared to GC models.

Feature pooling decreases features from 13,316 to 100 without losing accuracy.

Different properties are influenced by distinct structural features, consistent with mechanistic insights.

Abstract

Accurate prediction of pure component physiochemical properties is crucial for process integration, multiscale modeling, and optimization. In this work, an enhanced framework for pure component property prediction by using explainable machine learning methods is proposed. In this framework, the molecular representation method based on the connectivity matrix effectively considers atomic bonding relationships to automatically generate features. The supervised machine learning model random forest is applied for feature ranking and pooling. The adjusted R2 is introduced to penalize the inclusion of additional features, providing an assessment of the true contribution of features. The prediction results for normal boiling point (Tb), liquid molar volume, critical temperature (Tc) and critical pressure (Pc) obtained using Artificial Neural Network and Gaussian Process Regression models confirm the accuracy of the molecular representation method. Comparison with GC based models shows that the root-mean-square error on the test set can be reduced by up to 83.8%. To enhance the interpretability of the model, a feature analysis method based on Shapley values is employed to determine the contribution of each feature to the property predictions. The results indicate that using the feature pooling method reduces the number of features from 13316 to 100 without compromising model accuracy. The feature analysis results for Tb, Tc, and Pc confirms that different molecular properties are influenced by different structural features, aligning with mechanistic interpretations. In conclusion, the proposed framework is demonstrated to be feasible and provides a solid foundation for mixture component reconstruction and process integration modelling.