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Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers

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Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of Molecules.

Yan Xiang¹, Yu-Hang Tang², Hongyi Liu¹

¹School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

The Journal of Physical Chemistry. A

|May 17, 2021

Summary

This summary is machine-generated.

This study introduces a novel graph representation for molecules with Gaussian process regression (GPR) to accurately predict thermodynamic properties. The method achieves experimental precision and quantifies prediction reliability.

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Area of Science:

Computational chemistry
Materials science
Chemical engineering

Background:

Predicting thermodynamic properties of pure substances is crucial for chemical process design.
Existing methods often lack accuracy or transferability across different phases.
Accurate property prediction aids in material discovery and process optimization.

Purpose of the Study:

To develop a novel graph representation for molecules to predict thermodynamic properties.
To implement a Gaussian process regression (GPR) model utilizing this graph representation.
To assess the accuracy and reliability of the GPR model for single, double, and triple phase properties.

Main Methods:

A transferable molecular graph representation was developed as input for a marginalized graph kernel.
Gaussian process regression (GPR) models were employed, incorporating radial basis function kernels for temperature and pressure.
The model predicted critical temperature, vapor-liquid equilibrium (VLE) density, and pressure-temperature density for pure substances.

Main Results:

The GPR model achieved prediction accuracy comparable to experimental measurement precision.
The model demonstrated reliability through quantifiable posterior uncertainty for each prediction.
The proposed graph representation and GPR approach outperformed Morgan fingerprints and graph neural networks.

Conclusions:

The novel molecular graph representation coupled with GPR offers a highly accurate and reliable method for predicting thermodynamic properties.
This approach provides a robust tool for computational chemistry and materials science.
The ability to quantify prediction uncertainty enhances the practical applicability of the model.