We introduce CAVIAR, new scientific software that is developed for molecular simulation of ionic-liquids or charged colloids inside conductive boundaries. CAVIAR imports computer-aided-design geometries and uses them to simulate boundary walls. Then based on this geometry, a finite element mesh is generated and utilized for solving the Poisson equation. To avoid the complexity due to the singularity of point charges, we propose a new method, using the advantages of the superposition theorem of the linear partial differential equations. Within this paper, the CAVIAR structure, its features, and basic scientific algorithms are discussed.
Ionic-liquids (IL) inside conductive porous media can be used to make electrical energy storage units. Many parameters such as the shape of the pores and the type of IL affect the storage performance. In this work, a simple IL model inside two geometrically different slit-pores is simulated and their capacitive properties are measured. The pores were of finite length, one of them was linear and the other had a convex extra space in the center. The molecular dynamics simulations are done for two, qualitatively, low and high molarities. The pores have been simulated for both initially filled or empty conditions. Differential capacitance, induced charge density, and IL dynamics are calculated for all of the systems.
Supercapacitors are one of the technologically impressive types of energy storage devices that are supposed to fill the gap between chemical batteries and dielectric capacitors in terms of power and energy density. Many kinds of materials have been investigated to be used as supercapacitors’ electrolytes to overcome the known limitations of them. The properties of polymer-based electrolytes show a promising way to defeat some of these limitations. In this paper, a simplified model of polymer-based electrolytes between two electrodes is numerically investigated using the Molecular Dynamics simulation. The simulations are conducted for three different Bjerrum lengths and a typical range of applied voltages. The results showed a higher differential capacitance compared to the cases using ionic-liquid electrolytes. Our investigations indicate a rich domain in molecular behaviors of polymer-based electrolytes that should be considered in future supercapacitors.
Transport of mesoscale particles due to driving flow fields or external forces on a periodic surface appears in many areas. Geometrical and physical characteristics of particles affect the velocities of the particles in these periodic landscapes. In this paper, we present a numerical simulation based on solving the Langevin equation for the meso-size particles subjected to the thermal fluctuations in a periodic array of optical traps. We consider the real-size particles which cause the partial trapping of particles in the optical traps. The particles are sorted for the size-dependency of particles’ trajectories. Our results are in good agreement with experiments.
In this paper, we developed a new PINN-based model to predict the potential of point-charged particles surrounded by conductive walls. As a result of the proposed physics-informed neural network model, the mean square error and R 2 score are less than 7% and more than 90% for the corresponding example simulation, respectively. Results have been compared with typical neural networks and random forest as a standard machine learning algorithm. The R 2 score of the random forest model was 70%, and a standard neural network could not be trained well. Besides, computing time is significantly reduced compared to the finite element solver.
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