Structure and Energetics of Chemically Functionalized Silicene: Combined Density Functional Theory and Machine...

Area of Science:

• Materials Science
• Computational Chemistry
• Surface Science

Background:

• Elemental adsorbates on two-dimensional (2D) materials are crucial for electronic, sensing, and energy applications.
• Silicene, a silicon allotrope analogous to graphene, offers tunable properties for such applications.
• Understanding adsorbate-surface interactions is vital for designing next-generation devices.

Purpose of the Study:

• To investigate atomic adsorption models on silicene using a hybrid density functional theory (DFT) and machine learning (ML) approach.
• To develop predictive models for adsorption geometry and energy on functionalized silicene surfaces.
• To establish a high-throughput screening framework for 2D material functionalization.

Main Methods:

• Employed spin-polarized density functional theory (DFT) to optimize nearly 2000 atomic adsorption models on silicene.
• Selected most stable configurations based on calculated adsorption energies.
• Trained machine learning (ML) models, including tree-based algorithms and neural networks, for predicting adsorption properties.

Main Results:

• Successfully predicted adsorption geometry (classification) and adsorption energy (regression) using trained ML models.
• Identified stable adsorption configurations for various elements and coverages on silicene.
• Demonstrated the efficacy of the hybrid DFT + ML approach for rapid screening.

Conclusions:

• The hybrid DFT + ML framework enables efficient high-throughput screening of element-functionalized silicene.
• This approach accelerates the discovery of 2D materials for electronic and catalytic applications.
• Provides a transferable methodology for surface modification strategies in device engineering.