Besugárzással létrehozott ponthiba és ponthibaagglomerátumok, valamint ezek optikai tulajdonságokra gyakorolt hatásának elméleti vizsgálata szilíciumkarbidban = Theoretical investigation of point defects, their agglomerates and their effects on optical properties in irradiated silicon carbide by means of quantum mechanical calculations

Kutatásaimban három fontos atomi folyamatra mutattam rá a besugárzott SiC-ban: az antisite-ok, a vakanciák, valamint a szén-intersticiálisok aggregációja. A számításaim részben egy időben mutatták ki a kísérletekkel együtt ezeket, vagy előre megjósolták. Megmutattam, hogy ezen hibák általában elektromosan aktívak, és korábban már részben észlelték azokat. A divakancia azonosítása PRL-ben jelent meg, illetve szén antisite-vakancia pár azonosítása is ugyanolyan fontos eredmény mind elméleti mind gyakorlati szempontból. A p-típusú adalékok és szén-intersticiálisok komplexumai szintén létrehozhatnak termikusan stabil, parazita hibákat számításaink szerint a besugárzott SiC-ban, amelyet később a kísérletek is megerősítettek. A fentiek mellett a foszfor donor CVD-beli növesztésének megértéséhez, valamint az azonosításához járultam lényegesen hozzá. Tisztáztuk, hogy melyek a SiC/SiO2 határfelületen előforduló legfontosabb hibák, és azok hogyan befolyásolják a SiC elektronszerkezetét. Emellett megvizsgáltuk egy hipotetikus szuperrács elektromos és optikai tulajdonságait, amely ú.n. polaritásváltásos hibákat tartalmaz. Megmutattuk, hogy nm alatti ultravékony 2D elektron- és lyukgázt lehet így létrehozni. Emellett ez egy polarizációs szuperrácsot alkot, ahol az effektív tiltottsáv-szélességet lehet szabályozni. Emiatt különleges nem-lineáris optikai tulajdonságokkal is rendelkezik. Számításaink szerint atomi rétegleválasztás módszerével a fenti szuperrács megvalósítható. | In my studies I pointed out three basic processes at atomistic level in irradiated SiC: aggregation of antisites, vacancies and carbon self-interstitials. This was shown partly simultanuously with the experiments, or those have been predicted by my calculations. I found that these defects are usually electrically active, and some of them have been already detected. The identification of divacancy was published in PRL, while the identification of carbon antisite-vacancy pair is also very important result from both theoretical and technological point of view. Our calculations indicated that the complex of p-type dopants and carbon interstitials can also form thermally stable, parasite defects in irradiated SiC, that has been confirmed later in the experiments. Beside that our calculation significantly contributed to the understanding of doping of phosphorous in CVD chamber as well as in its identifiation. We found the most important defects at the interface of SiC/SiO2 and how those affected the electronic structure of SiC. In addition, we have investigated the electrical and optical properties of an hypothetical superlattice that contains so-called polarity-change defects. We showed that 2D electron and hole gases are formed under nm thickness. This forms also a polarization superlattice, in that the effective band gap can be controlled. It possesses peculiar non-linear optical properties. Our calculations showed that this superlattice can be grown by atomic layer epitaxy

Multi-Agent Reinforcement Learning for Railway Rescheduling

Malfunctions, congestions, and accidents occur in every railway system from time to time, which influences the railway traffic on a given section of the system. The disturbance may cause inconvenience for several passengers and disruption in rail freight. Both the schedule and route of the affected trains must be modified to avoid further congestion and minimalize delays. The rigidity of the railway system (e.g., single tracks, vast distances without a service station, no viable alternative in case of malfunction) poses restrictions, unlike other transportation systems. Replanning schedules and train routes (called the railway rescheduling problem) is complex and demanding, even for human operators, as one must consider numerous factors. Thus, finding a satisfying solution poses a significant challenge. This paper presents a MARL-base (Multi-Agent Reinforcement Learning) solution that shows great potential for tackling this problem, even in the case of multiple connected stations

Environment Representations of Railway Infrastructure for Reinforcement Learning-Based Traffic Control

The real-time railway rescheduling problem is a crucial challenge for human operators since many factors have to be considered during decision making, from the positions and velocities of the vehicles to the different regulations of the individual railway companies. Thanks to that, human operators cannot be expected to provide optimal decisions in a particular situation. Based on the recent successes of multi-agent deep reinforcement learning in challenging control problems, it seems like a suitable choice for such a domain. Consequently, this paper proposes a multi-agent deep reinforcement learning-based approach with different state representational choices to solve the real-time railway rescheduling problem. Furthermore, comparing different methods, the proposed learning-based approaches outperform their competitions, such as the Monte Carlo tree search algorithm, which is utilized as a model-based planner, and also other learning-based methods that utilize different abstractions. The results show that the proposed representation has more significant generalization potential and provides superior performance

Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future

Multi-Agent Deep Reinforcement Learning (MADRL) for Solving Real-Time Railway Rescheduling Problem

The real-time railway rescheduling problem is a challenging task since several factors have to be considered when a train deviates from the initial timetable. Nowadays, the problem is solved by human operators, which is safe but not optimal. This paper proposes a novel state representation for the introduced control problem that enables the efficient utilization of Multi-Agent Deep Reinforcement Learning. To support our claim, a proof of concept network is implemented, and the performance of the trained agent is evaluated. The results show that our approach enables fast convergence and excellent performance, while the representation has the potential for solving much more complex networks

libMBD: A general-purpose package for scalable quantum many-body dispersion calculations

Many-body dispersion (MBD) is a powerful framework to treat van der Waals (vdW) dispersion interactions in density-functional theory and related atomistic modeling methods. Several independent implementations of MBD with varying degree of functionality exist across a number of electronic structure codes, which both limits the current users of those codes and complicates dissemination of new variants of MBD. Here, we develop and document libMBD, a library implementation of MBD that is functionally complete, efficient, easy to integrate with any electronic structure code, and already integrated in FHI-aims, DFTB+, VASP, Q-Chem, CASTEP, and Quantum ESPRESSO. libMBD is written in modern Fortran with bindings to C and Python, uses MPI/ScaLAPACK for parallelization, and implements MBD for both finite and periodic systems, with analytical gradients with respect to all input parameters. The computational cost has asymptotic cubic scaling with system size, and evaluation of gradients only changes the prefactor of the scaling law, with libMBD exhibiting strong scaling up to 256 processor cores. Other MBD properties beyond energy and gradients can be calculated with libMBD, such as the charge-density polarization, first-order Coulomb correction, the dielectric function, or the order-by-order expansion of the energy in the dipole interaction. Calculations on supramolecular complexes with MBD-corrected electronic structure methods and a meta-review of previous applications of MBD demonstrate the broad applicability of the libMBD package to treat vdW interactions