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Computational Modeling and Simulation: Predictive Modeling and Simulation of Nanostructured Hard Materials for Optimization and Design

Puspendu Barik1 and Sasadhar Majhi2*

1Materials Science and Engineering Program, College of Arts and Sciences, American University of Sharjah, Sharjah, United Arab Emirates

2Department of Chemistry (UG & PG Dept.), Trivenidevi Bhalotia College, Raniganj, Kazi Nazrul University, West Bengal, India

Abstract

This book chapter explores the application of computational modeling and simulation techniques in optimizing and designing nanostructured hard materials. The chapter delves into the intricacies of simulating the behavior of these materials to enhance their physical properties and performance, focusing on predictive modeling. The content concisely describes hard nanomaterials, a general idea of modeling and simulation with some essential strategies, and insights into leveraging computational tools for designing nanostructured hard materials with improved properties, offering a valuable resource for researchers and practitioners in materials science and engineering as well as those interested in computational modeling and simulation.

Keywords: Modeling and simulation, simulation strategies, computational chemistry, popular software, nanostructured hard materials, materials science impact

1.1 Introduction

The study of hard nanostructured materials (HNM) has been a developing field for more than five decades after its first discovery in the 80’s [1–3]. Nanomaterials are defined as substances possessing at least one dimension within the nanoscale range, usually ranging from 1 to 100 nanometers. They showcase distinctive characteristics such as innovative optical attributes, enhanced reactivity, and mechanical properties compared to their larger counterparts. These distinctive features arise from their diminutive size and elevated surface-to-volume ratio. A captivating field within the realm of chemistry is computational chemistry. This discipline harnesses computer simulations to address diverse chemical challenges, employing methodologies that play a crucial role in determining the structures and properties of intricate molecules and solids [4]. Computational modeling, a significant tool, assists in analyzing, processing, and managing the data to extract valuable information, and it is employed to study different physiochemical properties of nanomaterials containing shape, size, structure, surface, and porosity [5–8]. Molecular modeling, simulations, and computational approaches are handy tools in chemical engineering and the community as they can predict and explain experimentally noticed macromolecular structure, thermodynamics, and microscopic and macroscopic material properties [9, 10]. Investigative tools were furnished by theory, modeling, and simulation, which support nanotechnology [11]. Importantly, computational techniques are frequently applied to validate hypotheses that may not be available through classical experimentation.

In contemporary times, the incorporation of modeling and simulation holds substantial importance in the exploration of HNMs in interpreting experimental data, elucidating design principles, and making innovative property predictions for unfamiliar nanomaterial structures and dynamics, encompassing their interfaces across the quantum scale to the microscale. Thus, the theory and simulation approaches are now pivotal in chemistry, physics, and biology. However, to get a realistic and scientific understanding of the properties of the system without any misinterpretation, we must have good knowledge of designing simulations in studies of HNMs. For example, we must care about the design of a system at the nanoscale, choosing the most appropriate modeling and simulation method, using state-of-the-art parameters and accuracy, and accumulating enough statistics. In addition to these scientific aspects, it is also essential to consider the similarity or exact match of experimental findings and the reproducibility of the simulations. In the present book chapter, we have tried to describe a general introduction to hard nanomaterials, a basic idea of modeling and simulation with some essential strategies, and insights into leveraging computational tools for designing nanostructured hard materials with improved properties, offering a valuable resource for researchers and practitioners in materials science and engineering.

1.2 Nanostructured Hard Materials

Nanostructured materials involve a broad spectrum of materials engineered at the nanoscale, possessing unique properties and functionalities. At present, a significant portion of nanomaterials, such as transition metal chalcogenides, phosphides, nitrides, carbides, and boron nitride nanosheets, which fall under the category of two-dimensional nanomaterials, have garnered considerable interest due to their diverse appealing properties and applications, particularly in the fields of energy storage and conversion [12, 13]. However, the materials science community shows little interest in the transition metal borides, since nanomaterials are challenging to prepare [14]. Metal borides are applied in the industry, including superconductors (MgB2), hard magnets (Nd2Fe14B), and hard materials (TiB2) [15]. Conventional methods for synthesizing metal borides typically involve elevated temperatures and/or high pressures, requiring the combination of metal with boron, often achieved through arc melting at temperatures exceeding 2500°C. Research has explored alternative approaches for the low-temperature synthesis of nanocrystalline metal borides, specifically employing ion thermal flux synthesis in molten salts at temperatures below 1000°C [16]. Recent research invented a procedure for synthesizing nanocrystalline transition metal borides organized according to the Sn/SnCl2 redox chemistry [14, 15].

HNMs are typically classified into several categories based on their composition. Here, we consider broad categories concerning HNMs.

1.2.1 Metal Nanostructure

Metal nanostructures emerge from diverse noble metals such as Au, Ag, Pt, Pd, and combinations thereof, along with metal oxides, exhibiting a size range of 1–100 nm. These nanostructures manifest in various shapes, including spheres, nanorods, nano cubes, nanospheres, and nanowires [17–19] and with different morphology (0D, 1D, 2D, and 3D). Due to their sizedependent behavior, these structures display exceptional properties and find applications in various fields such as electronics, catalysis, optics, and biomedical engineering. Surface plasmons are collective and coherent electron oscillations confined at the dielectric–metal interface, inducing optical properties hardly achievable in other optical materials. Tuning the aspect ratios of anisotropic nanocrystals is beneficial for specific applications.

1.2.2 Semiconductor Nanostructure

Nanostructures in the semiconductor realm, typically within the size range of 1-100 nanometers, exhibit distinct electronic transitions akin to isolated atoms and molecules, making them valuable for diverse applications, including solar energy conversion, optoelectronic devices, imaging, and detection [20]. Semiconductor nanostructures, including quantum dots, nanowires, and nanocrystals, have unique properties different from bulk due to quantum confinement effects that arise when the nanostructure size is comparable to the de Broglie wavelength of the charge carriers [21–23]. Contemporary comprehension of semiconductors is rooted in quantum physics, elucidating the dynamics of electrons and holes within a crystal structure and lattice. The quantum confinement effects observed in semiconductor nanostructures result in size-dependent optical and electronic properties, specifically size-dependent absorption and emission featuring discrete electronic transitions. In contrast to solid-state crystals and small-molecule chromophores and fluorophores, semiconductor nanostructures showcase distinctive optical properties influenced by their structure, composition, and size, proving beneficial for the development of photovoltaic and light-emitting devices. Additionally, the deliberate introduction of impurities or the application of electrical fields or light can modify the electrical properties of semiconductor materials, rendering them suitable for applications such as amplification, energy conversion, and switching [21, 23]. Intrinsic semiconductors are very pure semiconductor materials, i.e., having an equal number of holes and electrons, whereas, in extrinsic semiconductors, minute quantities of impurities (with trivalent or pentavalent dopant to obtain p-type or n-type, respectively) are doped to manipulate their conductivity and properties.

1.2.3 Magnetic Nanostructure

Magnetic nanostructures represent a captivating domain at the forefront of material science with their unique properties and transformative potential [24–26]. At the nanoscale, materials with magnetic properties showcase an array of behaviors that differ significantly from their bulk counterparts. From nanoparticles and nanowires to intricate thin films and composite structures, these nanostructures exhibit fascinating magnetic phenomena, often shaped by their size, shape, and arrangement. Magnetic nanostructures find applications in various domains, including data storage, biomedical applications (like magnetic imaging and...