DFT and machine learning for predicting hydrogen adsorption energies on rocksalt complex oxides

The prediction of hydrogen adsorption energies on complex oxides by integrating DFT calculations and machine learning is considered. In particular, 14 descriptors for electronic and geometric properties evaluation are adapted within a 336 hydrogen adsorption energy dataset created. Supervised learni...

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Bibliographic Details
Published in:Theoretical chemistry accounts 2024-06, Vol.143 (6), Article 50
Main Author: Domínguez-Castro, Adrian
Format: Article
Language:English
Subjects:
Adsorption
Artificial neural networks
Atomic/Molecular Structure and Spectra
Brief Report
Chemistry
Chemistry and Materials Science
Inorganic Chemistry
Machine learning
Organic Chemistry
Physical Chemistry
Prediction models
Predictions
Supervised learning
Theoretical and Computational Chemistry
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Summary:The prediction of hydrogen adsorption energies on complex oxides by integrating DFT calculations and machine learning is considered. In particular, 14 descriptors for electronic and geometric properties evaluation are adapted within a 336 hydrogen adsorption energy dataset created. Supervised learning techniques were explored to establish an accurate predictive model. With the deep neural network results, a MAE of about 0.06 eV is achieved. This research highlights the synergistic potential of DFT and machine learning for accelerating the exploration of materials for catalysis.
ISSN:1432-881X
1432-2234
DOI:10.1007/s00214-024-03124-x