Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

Area of Science:

• Surface Science
• Computational Chemistry
• Materials Science

Background:

• Determining atomic structures at organic-inorganic interfaces is difficult with existing methods.
• Interpreting complex adsorbate structures from microscopy and atomistic simulations is limited by high-dimensional phase spaces.

Purpose of the Study:

• To introduce the Bayesian Optimization Structure Search (BOSS) method for efficient identification of non-planar adsorbate structures.
• To apply BOSS with density-functional theory (DFT) to find stable adsorbate structures of (1S)-camphor on the Cu(111) surface.

Main Methods:

• Utilized the novel Bayesian Optimization Structure Search (BOSS) algorithm.
• Employed density-functional theory (DFT) simulations for structural analysis.
• Investigated the adsorption of (1S)-camphor on the Cu(111) surface.

Main Results:

• Identified eight unique types of stable camphor adsorbate structures.
• Determined the global minimum structure where camphor chemisorbs via oxygen to the Cu(111) surface.
• Found physisorbed structures where camphor interacts via its hydrocarbons.

Conclusions:

• The BOSS method provides an efficient approach for describing complex surface structures.
• This cross-disciplinary tool aids in understanding and tuning the functionality of advanced materials.
• Accurate structural identification is crucial for predicting and controlling material properties.