Starting with a microscopic (individual-based) Brownian dynamics model of charged particles (ions), its macroscopic description is derived as a system of partial differential equations that govern the evolution of ion concentrations in space and time. The macroscopic equations are obtained in the form of the Poisson-Nernst-Planck system. A multi-resolution method for simulating charged particles is then developed, combining the detailed Brownian dynamics model in a part of the computational domain with coarser macroscopic equations in the remainder. The strengths, limitations, and applicability of microscopic, macroscopic, and multi-resolution simulation approaches are demonstrated through an illustrative model comprising a system of Na$^+$ and Cl$^-$ ions.
For experimental and simulated solar cells and modules discrete current-voltage data sets are measured. To evaluate the quality of the device, this data needs to be fitted, which is often achieved within the single-diode equivalent-circuit model. This work offers a numerically robust methodology, which also works for noisy data sets due to its generally formulated initial guess. A Levenberg-Marquardt algorithm is used afterwards to finalize the fitting parameters. The source code and an executable version of the methodology are published under https://github.com/Pixel-95/SolarCell_DiodeModel_Fitting on GitHub. This work explains the underlying methodology and basic functionality of the program.
Two of the most widely used Langevin integrators for molecular dynamics simulations are the GROMACS Stochastic Dynamics (GSD) integrator and the splitting method BAOAB. In this letter, we show that the GROMACS Stochastic Dynamics integrator is equal to the less frequently used splitting method BAOA. It immediately follows that GSD and BAOAB sample the same configurations and have the same high configurational accuracy. Our numerical results indicate that GSD/BAOA has higher kinetic accuracy than BAOAB.
A parallelized quantum dynamics package using the Smolyak algorithm for general molecular simulation is introduced in this work. The program has no limitation of the Hamiltonian form and provides high flexibility on the simulation setup to adapt to different problems. Taking advantage of the Smolyak sparse grids formula, the simulation could be performed with high accuracy, and in the meantime, impressive parallel efficiency. The capability of the simulation could be up to tens of degrees of freedom. The implementation of the algorithm and the package usage are introduced, followed by typical examples and code test results.
AbstractTetrabutylammonium is a phase‐transfer agent commonly used in PET radiochemistry. Its toxicity makes its quantification mandatory. However, the official HPLC method for tetrabutylammonium analysis reported in the European Pharmacopoeia (Ph. Eur.) apparently fails to achieve the described separation in most new generation reverse‐phase columns. The study highlights the differences in separation achievable by varying some of the chromatographic conditions, such as temperature, eluent composition and ion‐pairing agent concentration. In the end, variations to the method within the limits allowed by the Ph. Eur. were not sufficient to overcome the problem, thus forcing to a more radical change of the organic component of the mobile phase.
Dmitry Levko, Robert R. Arslanbekov, Vladimir I. Kolobov
The expansion of non-ideal copper plasma into vacuum is analyzed for the conditions typical to explosive electron emission in vacuum arcs. The gas-dynamic model solves the Euler equations with an equation of state (EoS) for weakly non-ideal plasma taking into account the ionization energy correction due to electron-ion Coulomb coupling. We have obtained that the EoS has insignificant influence on the plasma expansion, when the plasma properties and composition change drastically. Based on our simulation results, the validity of the 'frozen' state theory often used in the vacuum arcs plasma diagnostics is questionable.
A state-of-the-art method that combines a quantum computational algorithm and machine learning, so-called quantum machine learning, can be a powerful approach for solving quantum many-body problems. However, the research scope in the field was mainly limited to organic molecules and simple lattice models. Here, we propose a workflow of quantum machine learning applications for periodic systems on the basis of an effective model construction from first principles. The band structures of the Hubbard model of graphene with the mean-field approximation are calculated as a benchmark, and the calculated eigenvalues show good agreement with the exact diagonalization results within a few meV by employing the transfer learning technique in quantum machine learning. The results show that the present computational scheme has the potential to solve many-body problems quickly and correctly for periodic systems using a quantum computer.