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This is a Quantitative Study study.

September 16, 2025Digital DiscoveryOpen Access

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states

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The model accurately predicts reaction barrier heights, enhancing understanding of reactivity.
Using graph neural networks and machine learning improves the prediction accuracy significantly.
The method facilitates on-the-fly predictions of transition states, essential for reaction design.
This approach has implications for optimizing chemical reactions and advancing material science.

Implication

Graph neural networks predict reaction barrier heights in chemical processes, suggesting advances in reaction design.

Abstract

The accurate prediction of reaction barrier heights is crucial for understanding chemical reactivity and guiding reaction design. Recent advances in machine learning (ML) models, particularly graph neural networks, have shown...

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Cite This Study

Karwounopoulos et al. (2025) studied this question.

synapsesocial.com/papers/68d4507d31b076d99fa57ea6 https://doi.org/10.1039/d5dd00240k

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Implication

Graph neural networks predict reaction barrier heights in chemical processes, suggesting advances in reaction design.

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states

Key Points

Implication

Abstract

Discussion

Cite This Study

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states

Key Points

Implication

Abstract

Discussion

Cite This Study

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