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- W4361009780 abstract "Graphene has attracted significant interest due to its unique properties. Herein, we built an adsorption structure selection workflow based on a density functional theory (DFT) calculation and machine learning to provide a guide for the interfacial properties of graphene. There are two main parts in our workflow. One main part is a DFT calculation routine to generate a dataset automatically. This part includes adatom random selection, modeling adsorption structures automatically, and a calculation of adsorption properties. It provides the dataset for the second main part in our workflow, which is a machine learning model. The inputs are atomic characteristics selected by feature engineering, and the network features are optimized by a genetic algorithm. The mean percentage error of our model was below 35%. Our routine is a general DFT calculation accelerating routine, which could be applied to many other problems. An attempt on graphene/magnesium composites design was carried out. Our predicting results match well with the interfacial properties calculated by DFT. This indicated that our routine presents an option for quick-design graphene-reinforced metal matrix composites." @default.
- W4361009780 created "2023-03-30" @default.
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- W4361009780 date "2023-03-26" @default.
- W4361009780 modified "2023-10-16" @default.
- W4361009780 title "Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning" @default.
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- W4361009780 doi "https://doi.org/10.3390/ma16072633" @default.
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