Python-based Simulation Tool for Kinetic Monte Carlo

Simulation tools are highly needed for testing or designing nanotechnology in university research projects.The problem in the current simulation tool used in our research, which is conducted by Prof.Garcia in MSE department at Purdue, is that users cannot observe the changing numbers of diffusivities during Vacancy Diffusion simulations between different materials. Also, there is no Graphic User Interface for the simulation tools.To solve the problem, the Virtual Kinetics of Materials Laboratory program is used to create the Graphic User Interface. Also, GTK+ toolkit has been used to create a pop-up window displaying updated diffusivities during the simulation. For user purpose, the pop-up window has been set to display a plot of diffusivity versus simulation time. During the programming process, several problems were found and solved by using GTK+ main loop and functions in Matplotlib plotting library. Also, there were also design problems about the Graphic User Interface on areas such as error checking and user experience. All the problems have been successfully solved, and the program has been tested and proved to be successful

Comparative Evaluation Research on the Carrying Capacity of Multi-regional Distribution Network Owner Project Departments Based on Combined Weights

This paper proposes a comparative evaluation method for the carrying capacity of the owner's project department based on combined weights. This method first combines the current grid companies’ requirements for the construction of the owner’s project department and the current distribution network construction management issues to systematically construct the owner’s project department’s carrying capacity evaluation index system. Through comprehensive evaluation and collection of indicator-related data, it is determined based on the coefficient of variation method. The objective weight of the indicator is determined based on the analytic hierarchy process; then the integrated scoring model is used to comprehensively calculate the normalized indicator value and weight value to obtain the evaluation analysis result; finally, the method is verified by empirical analysis Effectiveness

Vapor-assisted deposition of highly efficient, stable black-phase FAPbI3 perovskite solar cells

Mixtures of cations or halides with FAPbI3 (where FA is formamidinium) lead to high efficiency in perovskite solar cells (PSCs) but also to blue-shifted absorption and long-term stability issues caused by loss of volatile methylammonium (MA) and phase segregation. We report a deposition method using MA thiocyanate (MASCN) or FASCN vapor treatment to convert yellow δ-FAPbI3 perovskite films to the desired pure α-phase. NMR quantifies MA incorporation into the framework. Molecular dynamics simulations show that SCN- anions promote the formation and stabilization of α-FAPbI3 below the thermodynamic phase-transition temperature. We used these low-defect-density α-FAPbI3 films to make PSCs with >23% power-conversion efficiency and long-term operational and thermal stability, as well as a low (330 millivolts) open-circuit voltage loss and a low (0.75 volt) turn-on voltage of electroluminescence

Hybrid representations in 3D vision

Equipping machines with the ability to understand and process visual content from 3D sensors is important for enabling them to reason about the inherently 3D world we live in. Due to the high cost of 3D scanners, the availability of large-scale 3D dataset used to be scarce. Existing data-driven approaches have been mainly focusing on how to leverage 2D images for 3D understanding, while raw 3D scans are mainly used for research in geometric reconstruction. Recently, with cheaper hardware and hence broader availability of consumer-grade 3D cameras (e.g. Microsoft Kinect, Intel RealSense, iPhone12 Pro Max), several large-scale 3D datasets have been created. These datasets cover a variety of object categories and different indoor/outdoor environments--some in the form of raw scans, and some as reconstructed 3D meshes, raising unique challenges and opportunities for developing novel data-driven approaches. Data-driven 3D vision as a field has experienced an increasing amount of research interest. In this thesis, we look at a unique field in 3D visual learning: data representation. 3D data can be represented in different forms, including point clouds, voxels and meshes, each having its own representational or computational advantages. Existing work in 3D vision has studied how to separately leverage each representation for various downstream tasks. The problem of how to select "good" input and output representations for a particular visual learning task has become an important research topic. However, since 3D data can be transformed into one another, it is not a constraint to choose between representations, but rather, we should develop algorithms that can leverage different or multiple representations at the same time. In this work, we study the benefits of employing multiple data representations, namely hybrid representations, to solve various 3D vision problems. We show three major benefits of applying hybrid representations in this thesis: 1) Joint Learning from Multi-representation Supervisions; 2) Complementary Feature Learning; 3) Self-supervision Constraints for Unsupervised Learning. We demonstrate learning frameworks for indoor scene modeling, novel view image synthesis, sparse view 3D reconstruction, 3D object detection, 3D scene segmentation, and self-supervised feature pretraining. In each framework, hybrid representations serve as an essential component and significantly improve the performance in each task.Computer Science

Comparative Evaluation Research on the Carrying Capacity of Multi-regional Distribution Network Owner Project Departments Based on Combined Weights

This paper proposes a comparative evaluation method for the carrying capacity of the owner's project department based on combined weights. This method first combines the current grid companies’ requirements for the construction of the owner’s project department and the current distribution network construction management issues to systematically construct the owner’s project department’s carrying capacity evaluation index system. Through comprehensive evaluation and collection of indicator-related data, it is determined based on the coefficient of variation method. The objective weight of the indicator is determined based on the analytic hierarchy process; then the integrated scoring model is used to comprehensively calculate the normalized indicator value and weight value to obtain the evaluation analysis result; finally, the method is verified by empirical analysis Effectiveness