A density-functional-theory-based and machine-learning-accelerated hybrid method for intricate system catalysis
Being progressively applied in the design of highly active catalysts for energy devices, machine learning (ML) technology has shown attractive ability of dramatically reducing the computational cost of the traditional density functional theory (DFT) method, showing a particular advantage for the sim...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2021-08-01
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| Series: | Materials Reports: Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666935821000811 |