Magnetic on-surface assemblies predicted from a pious computational method

TL;DR

This paper introduces a novel computational approach combining genetic algorithms and MCMC to predict the self-assembly of magnetic molecular layers on surfaces, aligning well with experimental data and enabling design of assemblies with unique magnetic properties.

Contribution

It presents a new computational method that improves prediction accuracy for molecular self-assembly and demonstrates how ligand asymmetry can produce assemblies with disordered magnetic moments.

Findings

Predicted assemblies match experimental results.

Asymmetry in ligands induces magnetic disorder.

Method outperforms standard MCMC in accuracy.

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 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.