A Machine Learning-Accelerated Density Functional Theory (ML-DFT) Approach for Predicting Atomic Adsorption Energies on Monometallic Transition Metal Surfaces for Electrocatalyst Screening
The global mission to reduce fossil fuel consumption has led to the escalating demand for electrochemical energy storage (EES) devices such as fuel cells and batteries. Computational techniques like Density Functional Theory (DFT) have recently been coupled with Machine Learning (ML) for high-throug...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIDIC Servizi S.r.l.
2022-09-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/12679 |