Wroclawskie Centrum Sieciowo-Superkomputerowe

We investigate the conditions when noble-gas hydrides can be found in real environments and report on the preparation and identification of the HXeBr···CO(2) complex in a xenon matrix and HXeBr in a carbon dioxide matrix. The H-Xe stretching mode of the HXeBr···CO(2) complex in a xenon matrix is observed at 1557 cm(-1), showing a spectral shift of +53 cm(-1) from the HXeBr monomer. The calculations at the CCSD(T)/aug-cc-pVTZ-PP(Xe,Br) level of theory give two stable structures for the HXeBr···CO(2) complex with frequency shifts of +55 and +103 cm(-1), respectively. On the basis of the calculations, the experimentally observed band is assigned to the more stable structure with a "parallel" geometry. The HXeBr molecule was prepared in a carbon dioxide matrix and has the H-Xe stretching frequency of 1646 cm(-1), meaning a strong matrix shift and stabilization of the H-Xe bond. The deuterated species DXeBr in a carbon dioxide matrix absorbs at 1200 cm(-1). This is the first case where a noble-gas hydride is prepared in a molecular solid. The thermal stabilities of HXeBr and HXeBr···CO(2) complex in a xenon matrix and HXeBr in a carbon dioxide matrix were examined. We have found a high thermal stability of HXeBr in carbon dioxide ice (at least up to 100 K), i.e., under conditions that may occur in nature.

Abstract The efficient management of complex production systems is a challenge in today’s logistics. In the field of intelligent and sustainable logistics, the optimization of production batches, especially in the context of a rapidly changing product range, requires fast and precise computational solutions. This paper explores the potential of quantum computers for solving these problems. Traditional computational methods are often limited when it comes to optimizing complex logistics systems. In response to these challenges, the paper proposes the use of a hybrid algorithm that combines quantum technologies with classical computational methods. Such integration allows the computational power of both types of technologies to be harnessed, leading to faster and more efficient identification of optimal solutions. In this work, we consider the knapsack problem, a classic NP-hard optimization problem that is commonly used to verify the effectiveness of new algorithm construction methods. The algorithm presented is based on the Branch and Bound method and aims to ensure solution optimality in the context of the non-determinism of quantum computers. Within the algorithm, computations are performed alternately on a classical processor and a quantum processor. In addition, the lower and upper bounds of the objective function are computed in constant time using the D-Wave quantum machine.

We propose the new framework of the distributed tabu search metaheuristic designed to be executed using a multi-GPU cluster, i.e. cluster of nodes equipped with GPU computing units. We propose a hybrid single-walk parallelization of the tabu search, where hybridization consists in examining a number of solutions from a neighborhood concurrently by several GPUs (multi-GPU). The methodology is designed to solve the flexible job shop scheduling problem, diffcult problem of discrete optimization.

We report a systematic analysis of the intermolecular interactions of cationic ethidium intercalated into a UA/AU step of RNA for a single conformation based on crystallographic coordinates. Interaction energies at the MP2/6-31G** level were partitioned into electrostatic, exchange, delocalization, and correlation components. Various pairwise interaction models built from chemically intuitive fragments reproduce within a few percent values obtained when treating the intercalation site as a whole. Gas phase results are very sensitive to the charge state of the two phosphate groups, with the electrostatic term nearly tripling when the counterions are removed. But this is largely compensated by solvation, an effect represented here within the polarizable continuum model. In a few cases, more diffuse and larger basis sets as well as QCISD(T) corrections were applied in an effort to estimate plausible ethidium-nucleobase electron correlation effects.

This work focussed on thermal comfort as the basis to control indoor conditions. Its objective is a method to determine thermal preferences of office occupants. The method is based on detection of thermal events. They occur when indoor conditions are under control of occupants. Thermal events are associated with the use of local heating/cooling sources which have user-adjustable settings. The detection is based on Fourier analysis of indoor temperature time series. The relevant data is collected by temperature sensor. We achieved thermal events recognition rate of 86 %. Conditions when indoor conditions were beyond control were detected with 95.6 % success rate. Using experimental data it was demonstrated that the method allows to reproduce key elements of temperature statistics associated with conditions when occupants are in control of thermal comfort.

Abstract We present the development of the method for the refitting the ReaxFF parameters for a system consisting of the mixed transition metal oxides. We have tested several methods allowing to calculate the differences between the vectors of the forces acting on atoms obtained from the reference DFT simulation and the parameters-dependent ReaxFF. We conclude that the footrule method yields the best parameters among the investigated options. We then validate the parameters using the system consisting of the hematite supported (TiO 2 ) n clusters. The results indicate the refitted parameters allow to obtain much better geometries of the clusters upon MD simulation on the ReaxFF level, and despite the short timescale lead to the stable structures.

Committor analysis is a powerful, but computationally expensive, tool to study reaction mechanisms in complex systems.<br> The committor can also be used to generate initial trajectories for transition path sampling, a less-expensive technique<br> to study reaction mechanisms. The main goal of the project was to facilitate an implementation of committor analysis<br> in the software application OpenPathSampling (http://openpathsampling.org/) that is performance portable across a<br> range of HPC hardware and hosting sites. We do this by the use of hardware-enabled MD engines in OpenPathSampling<br> coupled with a custom library extension to the data analytics framework Dask (https://dask.org/) that allows for the<br> execution of MPI-enabled tasks in a steerable High Throughput Computing workflow. The software developed here is being used to generate initial trajectories to study a conformational change in the main protease of the SARS-CoV-2 virus, which causes COVID-19. This conformational change may regulate the accessibility of the active site of the main protease, and a better understanding of its mechanism could aid drug design.