(54g) Unsupervised Clustering of Existing Simulation Studies on Asphaltene Aggregation

Asphaltenes are defined as a solubility class rather than a singular family of molecular compounds, therefore there is no unique molecular structure that corresponds to asphaltene. Perhaps as a consequence of this definition, direct probing of physical and chemical properties of asphaltene molecules has been historically challenging in the laboratory. It was only in the past few years that experimental imaging of asphaltene structures resolved representative molecular geometries. Could the identification deriving from these experiments be useful for moving forward our collective understanding of the properties of asphaltenes? Is the experimental identification of numerous asphaltene molecules implying that simulations should consider all these compounds for providing useful insights? Is it possible that the community has been considering a limited sample of asphaltene molecules when attempting to shed light on asphaltene behavior? This focused review attempts to address these questions with the help of unsupervised clustering. The results demonstrate that the experimental asphaltene structures can be clustered into four groups that resolve different physical properties of asphaltenes. Then we probe the existing literature to assess whether compounds representative of the molecules experimentally identified as 'asphaltenes' using non-contact atomic force microscopy (nc-AFM) (Schuler, B., et al., J. Am. Chem. Soc. 2015, 137, 9870; Schuler, B., et al., Energy Fuels 2017, 31, 6856) have been investigated via simulations. We compare structures used in computational studies found in the literature against the experimental geometries, and summarize their context and findings. The exercise reveals that only a few asphaltene molecular structures have been studied while several asphaltene structures have been overlooked in published simulation works. While this is due to our collective efforts to study a 'representative' structure for asphaltenes, our analysis reveals a bias in the systems that have been simulated, which leads to suggesting possible directions for future studies that might shed light on the mechanisms responsible for asphaltene precipitation.