We employ all-atom molecular dynamics simulations up to microsecond time scales to study diffusion of fullerene nanoparticles (C60 and its derivative PCBM) in a polyimide matrix above its glass transition temperature. A detailed examination of the fullerene mobility in the embedding polymer system reveals the presence of different diffusion regimes (ballistic, subdiffusive, and normal diffusive). The microscopic origin of the observed subdiffusive regime is discussed by comparing the behavior to the one displayed by different anomalous diffusion processes, namely, continuous time random walk (CTRW), random walk on a fractal (RWF), and fractional Langevin equation (FLE). A series of statistical tests suggests that the FLE framework is the more appropriate one to describe subdiffusion of fullerenes in our system. Furthermore, a comprehensive analysis of the self-part of the van Hove function shows that the normal diffusion regime observed at long times displays a nonclassical behavior characterized by the simultaneous presence of several Gaussian peaks. We ascribe this behavior to a mechanism of diffusion by hopping. Until recently, it was commonly believed that hopping of tracer particles in polymer systems is only relevant for particle sizes that are of the order of the reptation tube diameter (d T), while smaller particles are considered to slip through the entanglement mesh. However, our results provide direct evidence for hopping as a relevant mechanism for diffusion of particles whose sizes are commensurate with the correlation length (ξ) of the polymer system. These results emphasize the importance of local interactions at the atomic level for the understanding of nanoparticle dynamics in polymer melts.

Atomic-scale computational models of paraffins are critically assessed and compared.

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The stability of poly(3‐hexylthiophene) (P3HT) helical structure has been investigated in vacuo and in amorphous polymer surrounding via molecular dynamics‐based simulations at temperatures below and above the P3HT melting point. The results show that the helical chain remains stable at room temperature both in vacuo and in amorphous surrounding, and promptly loses its structure at elevated temperatures. However, the amorphous surrounding inhibits the destruction of the helix at higher temperatures. In addition, it is shown that the electrostatic interactions do not significantly affect the stability of the helical structure. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 2448–2456

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Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

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In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing "structure−property" relationships. Having focused on a particular class of heterocyclic polymers, namely polyimides (PIs), we developed a graph convolutional neural network (GCNN), being one of the most promising tools for working with big data, to predict the PI glass transition temperature T g as an example of the fundamental property of polymers. To train the GCNN, we propose an original methodology based on using a "transfer learning" approach with an enormous "synthetic" data set for pretraining and a small experimental data set for its fine-tuning. The "synthetic" data set contains more than 6 million combinatorically generated repeating units of PIs and theoretical values of their T g values calculated using the well-established Askadskii's quantitative structure−property relationship (QSPR) computational scheme. Additionally, an experimental data set for 214 PIs was also collected from the literature for training, fine-tuning, and validation of the GCNN. Both "synthetic" and experimental data sets are included into a PolyAskInG database (Polymer Askadskii's Intelligent Gateway). By using the PolyAskInG database, we developed GCNN which allows estimation of T g of PI with a mean absolute error (MAE) of about 20 K, which is 1.5 times lower than in the case of Askadskii QSPR analysis (33 K). To prove the efficiency and usability of the proposed GCNN architecture and training methodology for predicting polymer properties, we also employed "transfer learning" to develop alternative GCNN pretrained on proxy-characteristics taken from the popular quantumchemical QM9 database for small compounds and fine-tuned on an experimental T g values data set from PolyAskInG database. The obtained results indicate that pretraining of GCNN on the "synthetic" polymer data set provides MAE which is almost twice as low as that in the case of using the QM9 data set in the pretraining stage (∼41 K). Furthermore, we address the questions associated with the influence of the differences in the size of the experimental and "synthetic" data sets (so-called "reality gap" problem), as well as their chemical composition on the training quality. Our results state the overall priority of using polymer data sets for developing deep neural networks, and GCNN in particular, for efficient prediction of polymer properties. Moreover, our work opens up a challenge for the theoretically supported generation of large "synthetic" data sets of polymer properties for the training of the complex ML models. The proposed methodology is rather versatile and may be generalized for predicting other properties of different polymers and copolymers synthesized through the polycondensation reaction.