HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

TL;DR

This paper introduces HEPOM, a computational tool using graph neural networks to predict hydrolysis free energies under different pH conditions, aiming to accelerate chemical recycling and clean energy applications.

Contribution

HEPOM combines reaction templates and ab initio data with a GNN model to predict pH-specific hydrolysis free energies and products.

Findings

A diverse dataset of hydrolysis free energies was created using reaction templates and ab initio calculations.

The GNN model accurately predicts ΔG values for hydrolysis reactions across different pH conditions.

The framework automates reaction center identification and product generation for high-throughput screening.

Abstract

Hydrolysis is a fundamental family of chemical reactions
where
water facilitates the cleavage of bonds. The process is ubiquitous
in biological and chemical systems, owing to water’s remarkable
versatility as a solvent. However, accurately predicting the feasibility
of hydrolysis through computational techniques is a difficult task,
as subtle changes in reactant structure like heteroatom substitutions
or neighboring functional groups can influence the reaction outcome.
Furthermore, hydrolysis is sensitive to the pH of the aqueous medium,
and the same reaction can have different reaction properties at different
pH conditions. In this work, we have combined reaction templates and
high-throughput ab initio calculations to construct a diverse data
set of hydrolysis free energies. The developed framework automatically
identifies reaction centers, generates hydrolysis products, and utilizes
a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given
molecule. The long-term goal of the work is to develop a data-driven,
computational tool for high-throughput screening of pH-specific hydrolytic
stability and the rapid prediction of reaction products, which can
then be applied in a wide array of applications including chemical
recycling of polymers and ion-conducting membranes for clean energy
generation and storage.

Figures