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

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. A computational method for simulating on-surface molecular self-assembly is presented. It combines a breadth of advanced techniques (density functional theory, machine learning, genetic algorithms, Monte Carlo sampling) and achieves good agreement with experimental data. By applying this method to the case of asymmetric phthalocyanine molecules adsorbed on gold surfaces, the formation of supramolecular assemblies with novel magnetic bifunctionality is predicted. image