# Machine Learning Accelerated Global Search for Adsorption Geometries of Merocyanine Molecule on Hexagonal Boron Nitride

## TL;DR

This paper uses machine learning to efficiently find how a dye molecule adsorbs on a boron nitride surface, revealing preferred orientations and interactions.

## Contribution

A novel ML-assisted global search strategy combining MACE potentials and Bayesian optimization for adsorption geometry exploration.

## Key findings

- HB238 prefers a face-on orientation with sulfur atoms near hollow sites on hBN.
- The molecule shows no strong site selectivity, leading to a broad range of low-energy configurations.
- Two HB238 molecules align parallel and lie flat on the hBN surface.

## Abstract

The adsorption of the merocyanine dye HB238 on hexagonal boron nitride (hBN) was investigated using a machine learning (ML) assisted global search strategy. A series of MACE machine learning interatomic potentials with higher‐order equivariant message passing were finetuned on density functional theory (DFT) reference data for single and dimer adsorbate configurations, providing accurate surrogate models for the potential energy surface. The Bayesian Optimisation Structure Search (BOSS) was used to search over translational and rotational degrees of freedom of the adsorbed molecules, followed by full PBE/D3 optimisation of the most promising structures. The ML‐accelerated search revealed that HB238 prefers to adsorb in face‐on orientation on hBN surface with the sulfur atoms located near hollow sites; however, the molecule exhibits no strong site selectivity, giving rise to a broad ensemble of configurations within energies 0.1 eV above the global minimum. When two HB238 molecules are adsorbed, they align parallel to each other and lie flat on the surface. Overall, our results demonstrate that combining finetuned ML potentials with Bayesian optimisation enables an efficient and accurate exploration of complex adsorption landscapes and provides fundamental insights into the physisorption of dipolar dyes on 2D insulators. This combined MACE×BOSS approach can be easily extended to investigate organic molecular aggregates on 2D surfaces.

Schematic illustration of HB238 adsorption on hBN(001) explored using Bayesian optimization and a MACE potential. The workflow accelerates potential‐energy‐surface exploration while retaining DFT‐level accuracy for molecular adsorption.