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Graph Representations and Neural Network Architectures for Reaction Barrier Height Prediction

Year: 2025
Type: article
Abstract: Chemical reactions are fundamental transformations that drive countless natural phenomena and technological applications, with their barrier heights, the minimum energy required for a reaction to potentially proceed, being crucial for understanding reactions. Deep learning approaches for predicting reaction barrier heights have shown promise but typically rely on explicit atom-to-atom mapping information, which is often unavailable in real-world scenarios. This thesis systematically explores graph representations and neural network architectures for predicting reaction barrier heights without relying on explicit atom mapping. We show that by incorporating reaction-specific inductive biases into neural network architectures, we can significantly reduce the performance gap between mapped and unmapped representations. Our best mapped-representation using Principal Neighbourhood Aggregation on the Condensed Graph of Reaction achieves a mean absolute error of 4.32 ± 0.45 kcal/mol, while our proposed Reaction Graph Transformer operating without atom mapping information reaches 6.18 ± 0.30 kcal/mol. The performance gap narrows even further on single reaction-type datasets, demonstrating that reaction-specific architectures can effectively compensate for missing mapping information. This work establishes a pathway toward more practical reaction property prediction tools that can operate effectively when atom mapping information is unavailable, enabling broader application in computational chemistry and drug discovery. Additionally, we contribute a flexible, modular code base for reaction property prediction that facilitates experimentation with different representations and architectures.
DOI: 10.34726/hss.2025.128222
Institution: TU Wien
Language: English
Open Access status: green
OpenAlex ID: W6965226669
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