Jaiswal, Prabhat Kumar

Using all-atom molecular dynamics simulations, we report the structure and ion transport characteristics of a new class of solid polymer electrolytes that contain the biodegradable and mechanically stable biopolymer pectin. We used highly conducting ethylene carbonate (EC) as a solvent for simulating lithium-trifluoromethanesulfonimide (LiTFSI) salt containing different weight percentages of pectin. Our simulations reveal that the pectin chains reduce the coordination number of lithium ions around their counterions (and vice versa) because of stronger lithium-pectin interactions compared to lithium-TFSI interactions. Furthermore, the pectin is found to promote smaller ionic aggregates over larger ones, in contrast to the results typically reported for liquid and polymer electrolytes. We observed that the loading of pectin in EC-LiTFSI electrolytes increases their viscosity (η) and relaxation timescales (τc), indicating higher mechanical stability, and, consequently, a decrease of the mean squared displacement, diffusion coefficient (D), and Nernst-Einstein conductivity (σNE). Interestingly, while the lithium diffusivities are related to the ion-pair relaxation timescales as D+ ∼ τc−3.1, the TFSI− diffusivities exhibit excellent correlations with ion-pair relaxation timescales as D− ∼ τc−0.95. On the other hand, the NE conductivities are dictated by distinct transport mechanisms and scales with ion-pair relaxation timescales as σNE ∼ τc−1.85

Surface-directed spinodal decomposition (SDSD) is the kinetic interplay of phase separation and wetting at a surface. This process is of great scientific and technological importance. In this paper, we report results from a numerical study of SDSD on a chemically patterned substrate. We consider simple surface patterns for our simulations, but most of the results apply for arbitrary patterns. In layers near the surface, we observe a dynamical crossover from a surface-registry regime to a phase-separation regime. We study this crossover using layerwise correlation functions and structure factors and domain length scales.

We study a complex mixture with bidispersity in size and polydispersity in energy using computer simulation. The energy polydispersity between the bidisperse particles is introduced by considering random pair-wise interactions. Extensive molecular dynamics simulations are performed to compute potential energy and neighborhood identity ordering (NIO) parameter as a function of temperature for different size-ratios and concentrations of the two species by quenching it from a high-temperature fluid state to a solid state. Our findings demonstrate an enhancement of the neighborhood identity ordering on the addition of particles of different sizes, which also depends on particle concentration. Moreover, a comparatively higher increase in the NIO parameter is achieved by tuning the size-ratio of the particles. We also propose that the NIO parameter is a good marker to differentiate multicomponent systems (below the liquid to solid transition temperature) with different size-ratios and concentrations.

The key to the survival of a species lies in understanding its evolution in an ever-changing environment. We report a theoretical model that integrates frequency-dependent selection, mutation, and asexual reproduction for understanding the biological evolution of a host species in the presence of parasites. We study the host-parasite coevolution in a one-dimensional genotypic space by considering a dynamic and heterogeneous environment modeled using a fitness landscape. It is observed that the presence of parasites facilitates a faster evolution of the host population toward its fitness maximum. We also find that the time required to reach the maximum fitness (optimization time) decreases with increased infection from the parasites. However, the overall fitness of the host population declines due to the parasitic infection. In the limit where parasites are considered to evolve much faster than the hosts, the optimization time reduces even further. Our findings indicate that parasites can play a crucial role in the survival of its host in a rapidly changing environment.

Visual content has become the primary source of information, as evident in the billions of images and videos, shared and uploaded on the Internet every single day. This has led to an increase in alterations in images and videos to make them more informative and eye-catching for the viewers worldwide. Some of these alterations are simple, like copy-move, and are easily detectable, while other sophisticated alterations like reenactment based DeepFakes are hard to detect. Reenactment alterations allow the source to change the target expressions and create photo-realistic images and videos. While the technology can be potentially used for several applications, the malicious usage of automatic reenactment has a very large social implication. It is therefore important to develop detection techniques to distinguish real images and videos with the altered ones. This research proposes a learning-based algorithm for detecting reenactment based alterations. The proposed algorithm uses a multi-stream network that learns regional artifacts and provides a robust performance at various compression levels. We also propose a loss function for the balanced learning of the streams for the proposed network. The performance is evaluated on the publicly available FaceForen- sics dataset. The results show state-of-the-art classification accuracy of 99.96%, 99.10%, and 91.20% for no, easy, and hard compression factors, respectively.

We present simulation results from a comprehensive molecular dynamics (MD) study of surface-directed spinodal decomposition (SDSD) in unstable symmetric binary mixtures at wetting surfaces. We consider long-ranged and short-ranged surface fields to investigate the early stage wetting kinetics. The attractive part of the long-ranged potential is of the form V(z)∼z-n, where z is the distance from the surface and n is the power-law exponent. We find that the wetting-layer thickness R1(t) at very early times exhibits a power-law growth with an exponent α=1/(n+2). It then crosses over to a universal fast-mode regime with α=3/2. In contrast, for the short-ranged surface potential, a logarithmic behavior in R1(t) is observed at initial times. Remarkably, similar rapid growth is seen in this case too. We provide phenomenological arguments to understand these growth laws. Our MD results firmly establish the existence of universal fast-mode kinetics and settle the related controversy.

Machine learning has proven exceptionally competent in numerous applications of studying dynamical systems. In this article, we demonstrate the effectiveness of reservoir computing, a famous machine learning architecture, in learning a high-dimensional spatiotemporal pattern. We employ an echo-state network to predict the phase ordering dynamics of 2D binary systems - Ising magnet and binary alloys. Importantly, we emphasize that a single reservoir can be competent enough to process the information from a large number of state variables involved in the specific task at minimal computational training cost. Two significant equations of phase ordering kinetics, the time-dependent Ginzburg-Landau and Cahn-Hilliard-Cook equations, are used to depict the result of numerical simulations. Consideration of systems with both conserved and non-conserved order parameters portrays the scalability of our employed scheme.

This paper is the second in a two-part exposition on surface-directed spinodal decomposition (SDSD), i.e., the interplay of kinetics of wetting and phase separation at a surface which is wetted by one of the components of a binary mixture. In our first paper [P. Das, P. K. Jaiswal, and S. Puri, Phys. Rev. E 102, 012803 (2020)2470-004510.1103/PhysRevE.102.012803], we studied SDSD on chemically heterogeneous and physically flat substrates. In this paper, we study SDSD on a chemically homogeneous but morphologically patterned substrate. Such substrates arise in a vast variety of technological applications. Our goal is to provide a theoretical understanding of SDSD in this context. We present detailed numerical results for domain growth both inside and above the grooves in the substrate. The morphological evolution can be understood in terms of the interference of SDSD waves originating from the different surfaces comprising the substrate.