Publications by authors named "Artem Glova"
Article Synopsis
- Machine learning techniques, specifically principal component analysis (PCA) and diffusion maps (DM), are used to analyze complex datasets from molecular dynamics simulations of polylactide (PLA) and poly(3-hydroxybutyrate) (PHB) to evaluate glass transition temperature (Tg).
- Four molecular descriptors—radial distribution functions (RDFs), mean square displacements (MSDs), relative square displacements (RSDs), and dihedral angles (DAs)—are employed to facilitate this evaluation.
- The use of Gaussian Mixture Models (GMMs) to analyze the data reveals a clear distinction between melt and glass states and shows that Tg values derived from DM and certain descriptors align well with
Recent experiments and atomistic computer simulations have shown that asphaltene byproducts of oil refineries can serve as thermal conductivity enhancers for organic phase-change materials such as paraffin and therefore have the potential to improve the performance of paraffin-based heat storage devices. In this work, we explore how the size of the polycyclic aromatic cores of asphaltenes affects the properties of paraffin-asphaltene systems by means of atomistic molecular dynamics simulations. We show that increasing the size of the asphaltene core from 7-8 aromatic rings to ∼20 rings drastically changes the aggregation behavior of asphaltenes.
View Article and Find Full Text PDFAsphaltenes represent a novel class of carbon nanofillers that are of potential interest for many applications, including polymer nanocomposites, solar cells, and domestic heat storage devices. In this work, we developed a realistic coarse-grained Martini model that was refined against the thermodynamic data extracted from atomistic simulations. This allowed us to explore the aggregation behavior of thousands of asphaltene molecules in liquid paraffin on a microsecond time scale.
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