Lightning search algorithm: a comprehensive survey

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning

We develop a computational database, web-apps and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic band-energies for 674 non-metallic exfoliable 2D-materials, we generate 226779 possible heterostructures. We classify these heterostructures into type-I, II and III systems according to Anderson rule, which is based on the band-alignment with respect to the vacuum potential of non-interacting monolayers.We find that type-II is the most common and the type-III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the periodic table of constituent elements. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts.We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS2/WSe2, MoS2/h-BN, MoSe2/CrI3) to compare the band-alignment description with the predictions from Anderson rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we develop ML tools to predict band-alignment information for 2D materials. The web-apps, tools and associated data will be distributed through JARVIS-Heterostructure website (https://www.ctcms.nist.gov/jarvish).Our analysis, results and the developed web-apps can be applied to the screening and design applications, such as finding novel photocatalysts, photodetectors, and high-work function 2D-metal contacts

A user-friendly and accurate machine learning tool for the evaluation of the worldwide yearly photovoltaic electricity production

While traditional methods for modelling the thermal and electrical behaviour of photovoltaic (PV) modules rely on analytical and empirical techniques, machine learning is gaining interest as a way to reduce the time, expertise, and tools required by designers or experts while maintaining high accuracy and reliability. This research presents a data-driven machine learning tool based on artificial neural networks (ANNs) that can forecast yearly PV electricity directly at the optimal PV inclination angle without geographic restrictions and is valid for a wide range of electrical characteristics of PV modules. Additionally, empirical correlations were developed to easily determine the optimal PV inclination angle worldwide. The ANN algorithm, developed in Matlab, systematically and quantitatively summarizes the behaviour of eight PV modules in 48 worldwide climatic conditions. The algorithm's applicability and robustness were proven by considering two different PV modules in the same 48 locations. Yearly climatic variables and electrical/thermal PV module parameters serve as input training data. The yearly PV electricity is derived using dynamic simulations in the TRNSYS environment, which is a simulation program primarily and extensively used in the fields of renewable energy engineering and building simulation for passive as well as active solar design. Multiple performance metrics validate that the ANN-based machine learning tool demonstrates high reliability and accuracy in the PV energy production forecasting for all weather conditions and PV module characteristics. In particular, by using 20 neurons, the highest value of R-square of 0.9797 and the lowest values of the root mean square error and coefficient of variance of 14.67 kWh and 3.8%, respectively, were obtained in the training phase. This high accuracy was confirmed in the ANN validation phase considering other PV modules. An R-square of 0.9218 and values of the root mean square error and coefficient of variance of 31.95 kWh and 7.8%, respectively, were obtained. The results demonstrate the algorithm's vast potential to enhance the worldwide diffusion and economic growth of solar energy, aligned with the seventh sustainable development goal

Synthesis, film deposition, and characterization of quaternary metal chalcogenide nanocrystals for photovoltaic applications

2017 Fall.Includes bibliographical references.The production, storage, and subsequent consumption of energy are at the foundation of all human activity and livelihood. The theme of this dissertation is the pursuit of fundamentalunderstanding of the chemistry of materials that are used for energy production and storage. A strong emphasis is placed on a synthetic foundation that allows for systematic investigation into the fundamental chemistry that controls the applicable properties of the materials of interest. This dissertation is written in the "journals format" style—which is accepted by the Graduate School at Colorado State University—and is based on one peer-reviewed publication that has appeared in Chemistry of Materials as well as two manuscripts to be submitted, one to The Journal of Physical Chemistry C, and one to ACS Applied Materials and Interfaces. In order to create a context forthese publications, Chapters 1 and 3 provide an overview of the motivations for the projects, and then continue to detail the initial synthetic investigations and considerations for the two projects. In addition to recounting Mg nanocrystals synthetic refinement that was necessary for reproducible hydride kinetic analysis, Chapter 1 also briefly introduces some of the conventional models used for fitting of the hydriding kinetics data. Furthermore, initial investigations into the use of these models for our system are presented. Chapter 2 is a paper to be submitted to The Journal of Physical Chemistry C that describes the local and extended structure characterization of Mg nanocrystals (NCs) with a small amount of nickel added during synthesis. Ni has a dramatic effect on the de/hydriding kinetics of Mg NCs, and this chapter describes the use of a combination of multiple state-of-the-art characterization techniques to gain insight into the structural perturbations due to Ni inclusion in the Mg NCs. This insight is then used to establish the characteristics of Ni inclusion that results in the enhanced hydrogen absorption processes. Chapter 3 introduces the many considerations needed to be taken into account during the development of a novel synthesis for copper zinc tin chalcogenide colloidal nanocrystals. In addition to introducing synthetic approaches to achieve this goal, Chapter 3 also describes essential characteristics that need to be considered for further investigation into the properties of films made from the nanocrystals. Chapter 4 is a publication that appeared in Chemistry of Materials, that describes an approach to tuning the surface and ligand chemistry of Cu2ZnSnS4 nanocrystals for use as an absorber layer in next generation photovoltaic devices. The publication describes ligand exchange chemistry achieved via layer-by-layer dip-casting of nanocrystal thin films, and the effects that this exchange chemistry has on the resulting films. It also details the fabrication of full photovoltaic (PV) devices to characterize the benefits of controlling the surface chemistry can have on PV performance. Chapter 5 is a paper—to be submitted to ACS Applied Materials and Interfaces—that describes the investigations into how varying the chalcogen ratio (i.e., S:Se) leads to changes in the physical and electrical properties of thin films made from Cu2ZnSn(S1-xSex)4 (where 0 < x < 1) NCs. It highlights the novel synthetic procedure (detailed in chapter 3) that was required for a systematic, deconvoluted evaluation of S:Se composition on the materials optical and electronic properties. Moreover, the characteristics of full PV devices based on thin films of each stoichiometry (x=0 to x=1) are assessed to establish a relationship between composition and the materials performance