2022/04/21 by Daniel M. Packwood, Packwood, Daniel M.
Materials Science · Physics and Astronomy · #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Quantum many-body systems #Theoretical and Computational Physics
paper · pdf · doi:10.48550/arxiv.2204.09823
openalex publication_date 2022/04/21 · openalex created_date 2022/04/26 · openalex updated_date 2026/07/28
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 properties are required for building nanomaterials for specific device applications. In this paper, I simulate the self-assembly of metal phthalocyanine derivatives adsorbed to gold(111) surfaces using a detailed statistical mechanical model and a new computational method based upon genetic algorithms and Markov chain Monte Carlo (MCMC). This method yields predictions that are not only are superior to those of ordinary MCMC but also show good agreement with experimental results. Crucially, it is predicted that molecular assemblies displaying locally disordered magnetic moments - and having potential applications as non-Gaussian noise sources for device applications - can be realized by the simple strategy of introducing asymmetry into the phthalocyanine ligands.