Bi‐Functional On‐Surface Molecular Assemblies Predicted From a Multifaceted Computational Approach

Abstract

Abstract Molecular self‐assembly will not become a routine method for building nanomaterials unless our ability to predict the outcome of this process is dramatically improved. Even then, reliable strategies for realizing molecular assemblies with novel functionality are required for building nanomaterials for specific device applications. On the basis of a multifaceted computational approach that integrates several state‐of‐the‐art methods, this paper predicts that bi‐functional on‐surface assemblies of metal phthalocyanine molecules can be realized through the simple strategy of introducing asymmetry into the phthalocyanine ligands. This bi‐functionality arises from a combination of antiferromagnetic ordering within the assembly and presence of locally fluctuating magnetic moments, and has potential applications as non‐Gaussian noise sources in nanodevices.

Keywords

• density functional theory
• machine learning
• self‐assembly
• simulations
• surfaces