Deep Graph Kernel Learning for Material & Atomic Level Uncertainty Quantification in Adsorption Energy Prediction

Osman Mamun; Chenlu Yang; Shuwen Yue

doi:10.1039/d6dd00020g

January 1, 2025Digital DiscoveryOpen Access

Deep Graph Kernel Learning for Material & Atomic Level Uncertainty Quantification in Adsorption Energy Prediction

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Key Points

This research aims to enhance the prediction of adsorption energy using advanced graph neural networks to quantify uncertainty.
Utilized graph neural networks as surrogates for density functional theory calculations.
Applied kernel learning techniques to assess uncertainty in predictions.
Evaluated the model's accuracy in predicting adsorption energy.
Successfully improved prediction accuracy for adsorption energy.
Demonstrated effective uncertainty quantification in the predictions.
Showed potential for accelerating the discovery of catalytic materials.

Implication

Graph neural networks improve adsorption energy prediction in materials, suggesting enhanced discovery processes.

Abstract

Graph neural networks (GNNs) have emerged as powerful and efficient surrogates for computationally intensive density functional theory calculations and have greatly accelerated catalytic material discovery. However, their practical utility is...

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Cite This Study

Mamun et al. (2025) studied this question.

synapsesocial.com/papers/69c4cd3efdc3bde448919497 https://doi.org/10.1039/d6dd00020g

View Full Paper

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Deep Graph Kernel Learning for Material & Atomic Level Uncertainty Quantification in Adsorption Energy Prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

Deep Graph Kernel Learning for Material & Atomic Level Uncertainty Quantification in Adsorption Energy Prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

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