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A Computational Predictive Approach for Controlling the Morphology of Functional Molecular Aggregates on Substrates
Author(s) -
Lorenzoni Andrea,
Muccini Michele,
Mercuri Francesco
Publication year - 2019
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.201900156
Subject(s) - molecular dynamics , nanoscopic scale , materials science , nanotechnology , chemical physics , substrate (aquarium) , graphene , molecular electronics , molecule , deposition (geology) , diimide , biological system , chemistry , computational chemistry , paleontology , oceanography , perylene , organic chemistry , sediment , geology , biology
Full control of the growth dynamics and morphology of nanoscale molecular aggregates on substrates is a crucial factor for the development of novel materials and devices for technological applications. A novel computational approach based on molecular dynamics, which is able to predict the structure of nanoscale aggregates of organic small molecules on substrates as a function of growth and processing conditions, is presented. In the simulation protocol proposed, molecules are progressively added to the substrate, one by one, reproducing vacuum thermal deposition processes. The simulation parameters affect the structure of the configuration, and formation of molecular aggregates is spontaneously observed upon reaching a critical molecular density at the interface. Suitable simulation parameters allow to access different molecular aggregation morphologies on substrates, including crystalline phases, matching the structural variability observed in real fabrication conditions. This approach is benchmarked by simulating the morphology of aggregates of N , N ′‐ditridecylperylene‐3,4,9,10‐tetracarboxylic diimide (PTCDI‐C13), a prototypical n‐type semiconductor for organic electronics, on graphene, in different growth conditions. Simulations predict structural features of aggregates of PTCDI‐C13 on graphene that are in excellent agreement with experiments and, at the same time, provide a rationale and quantitative relationship between molecular structure and observed aggregation morphologies.