Accelerating Density Functional Calculation of Adatom Adsorption on Graphene via Machine Learning.

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 mod

SUBMITTER: Qu N 

PROVIDER: S-EPMC10095669 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature