Computational Technologies in Materials Science

Order Tramadol Mexico Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface Editors Contributors Chapter 1 Fabrication and Characterization of Materials 1.1 Introduction 1.2 Material Synthesis Techniques 1.2.1 Czochralski Technique 1.2.2 Chemical Vapor Transport Technique 1.2.3 Physical Vapor Deposition 1.2.3.1 Sputtering 1.2.3.2 Pulsed Laser Deposition 1.3 Nanofabrication 1.3.1 Photolithography 1.3.2 Electron-Beam Lithography 1.4 Materials Characterization 1.4.1 Scanning Electron Microscope 1.4.2 Transmission Electron Microscope 1.4.3 Scanning Tunneling Microscope References Chapter 2 Application to Advanced Materials Simulation 2.1 Introduction 2.2 The Entrance of DFT into Chemistry 2.2.1 Functional Exchange-Correlation 2.2.1.1 Local Density Approximation (LDA) 2.2.1.2 Generalized Gradient Approximation [GGA] 2.2.1.3 Meta-GGA 2.2.1.4 Hybrid Functionals: Hartree–Fock Exchanges in Functional Development 2.2.1.5 Recent Progress in Functionals 2.3 Application of DFT in Photovoltaics 2.3.1 Assessing the Fundamental Properties of Semiconductors Used in Photocatalysts and Photovoltaic Systems 2.3.1.1 Methodology 2.3.1.2 Results and Discussions 2.3.2 Investigated Efficiency of Photocatalysis in a Monolayer g-C[sub(3)]N[sub(4)]/CdS Heterostructure [89] 2.3.3 Perceptions from a DFT+U Investigation on Interfacial Interactions, Charge Transport, and Synergic Mechanisms in BiNbO[sub(4)]/MWO[sub(4)] (M = Cd and Zn) Heterostructures for Hydrogen Processing [96] 2.3.3.1 Computational Details 2.3.3.2 Results and Discussions 2.4 Conclusion and Future Prospects Acknowledgment References Chapter 3 Molecular Dynamics Simulations for Structural Characterization and Property Prediction of Materials 3.1 Introduction 3.1.1 Force Fields in Molecular Dynamics 3.1.2 Molecular Dynamics Algorithm 3.1.3 Parameters for MD Simulations 3.1.3.1 Starting Structure 3.1.3.2 Initial Temperature/Energy 3.1.3.3 Size of the Timestep 3.1.3.4 Length of a Simulation 3.2 Material Simulation at the Nanoscale and Bulk and Its Visualization 3.2.1 Atomsk to Make New Material Structures 3.2.2 LAMMPS Molecular Dynamics Simulator 3.2.2.1 Initialization 3.2.2.2 Atom Definition 3.2.2.3 Settings 3.2.2.4 Running a Simulation 3.2.2.5 Ensembles, Constraints, and Boundary Conditions 3.3 Property Prediction with Molecular Dynamics 3.3.1 Mechanical Properties 3.3.2 Thermal Properties 3.4 Post-Processing and the Visualization of the Output Data 3.4.1 Open Visualization Tool (OVITO) 3.4.2 Visual Molecular Dynamics (VMD) 3.5 Machine Learning for Performance Enhancement of Molecular Dynamics Simulations 3.5.1 Methodology of Applying Machine Learning in Molecular Dynamics Simulations 3.5.2 Machine Learning and Related Approaches to Enhance Sampling 3.6 Conclusion References Chapter 4 Desirability Approach-Based Optimization of Process Parameters in Turning of Aluminum Matrix Composites 4.1 Introduction: Background and Driving Forces 4.2 Selection of Materials 4.2.1 Matrix 4.2.2 Reinforcement 4.3 Experimental Procedure 4.3.1 Fabrication Method 4.3.2 Machining Process 4.4 Conduction of Tests and Obtained Results 4.4.1 Physical Tests 4.4.1.1 Surface Roughness Test 4.4.1.2 Roundness Test 4.4.2 Mechanical Tests 4.4.2.1 Hardness Test 4.4.2.2 Impact Test 4.5 Machine Learning Inspired Optimization 4.6 Conclusion References Chapter 5 Spark Plasma-Induced Combustion Synthesis, Densification, and Characterization of Nanostructured Magnesium Silicide for Mid Temperature Energy Conversion Energy Harvesting Application 5.1 Introduction 5.1.1 Global Evolution in Renewable Energy Technologies 5.1.2 Thermoelectrics Technology 5.1.3 Thermoelectrics – Advancements and Technological Growth 5.2 Magnesium Silicide 5.2.1 Crystallographic Structure and Band Gap Configuration 5.2.2 Phase and Thermodynamical Behaviour of Mg[sub(2)]Si 5.3 Spark Plasma Assisted Combustion Synthesis of Magnesium Silicide 5.4 Phase and Structural Characterisation of Mg-Si Milled Powders with Varying Bi Doping Concentration 5 4.1 Characterization of Spark Plasma Assisted In-Situ Synthesized Mg[sub(2)]Si[sub(1–x)] – Bx Compound 5.4.2 Analysis of Structure and Texture of Mg–[sub(2)]Si Compound 5.5 Thermoelectric Properties of the Mg[sub(2)]Si Compound 5.6 Conclusions References Chapter 6 The Role of Computational Intelligence in Materials Science: An Overview 6.1 Introduction 6.2 Prediction of Critical Temperature in Superconductors 6.3 Flaw Detection in 3D Composite Materials 6.4 Pharmaceutical Tableting Processes 6.5 Processing of Smart Laser Materials 6.6 Determination of Energy Bandgap of Semiconductors 6.7 Lattice Constant Prediction of Perovskites 6.8 Adsorption Isotherms of Metal Ions onto Zeolites 6.9 Material Constants of Composite Materials 6.10 Conclusion References Chapter 7 Characterization Techniques for Composites using AI and Machine Learning Techniques 7.1 Introduction 7.2 Microscopy 7.2.1 Optical Microscopy 7.2.2 Scanning Electron Microscopy (SEM) 7.2.3 Transmission Electron Microscopy (TEM) 7.2.4 Scanning Tunneling Microscopy (STM) 7.2.5 Field Ion Microscopy 7.2.6 Atomic Force Microscopy (AFM) 7.3 Spectroscopy 7.3.1 Ultraviolet Spectroscopy (UV-Spectroscopy) 7.3.2 Fourier Transform Infrared Radiation Spectroscopy (FTIR-Spectroscopy) 7.3.3 Optical Emission Spectroscopy (OES) 7.3.4 Energy Dispersive Spectroscopy (EDS) 7.3.5 X-ray Diffraction (XRD) 7.4 AI and ML Techniques for Material Characterization 7.4.1 Feature Mapping Using the Scale-Invariant Feature Transform (SIFT) Algorithm 7.4.2 Image Classification Using Machine Learning and Deep Learning 7.5 Conclusion References Chapter 8 Experimental Evaluation on Tribological Behavior of TiO[sub(2)] Reinforced Polyamide Composites Validated by Taguchi and Machine Learning Methods 8.1 Introduction 8.2 Polyamides 8.3 Polyamide 6 8.4 Chemical Composition of PA6 8.5 Features of PA6 8.6 Applications of PA6 8.7 Fillers 8.8 Materials Used 8.9 Preparation of the Test Specimen 8.9.1 Preparation of the PA6 Test Specimen 8.9.2 Various Stages of Preparation of the PA6 Composite Test Specimen 8.9.3 Proportions of the Fabricated PA6 Composite Test Specimen 8.10 Tribological Properties of PA6 Composites 8.10.1 Friction and Wear Rate 8.11 Analysis of the Tribological Performance of PA6 Mixtures 8.11.1 Co-Efficient of Friction for PA6 Mixtures 8.11.2 Rate of Wear 8.12 SEM Study of TiO[sub(2)]/PA6 Composites 8.13 Comparative Study on Validation 8.13.1 Experimental Design 8.13.2 Exploration of Experimental Design Results 8.13.3 ANOVA and Effects of Factors 8.14 Machine Learning 8.14.1 Linear Regression 8.14.2 Comparison of Wear Vs Weight% 8.14.3 Comparison of Wear Vs Speed 8.14.4 Comparison of Wear Vs Load 8.14.5 Validation of Experiment, Taguchi Method, and ML Algorithm 8.14.6 Comparison of the Taguchi Method and the ML Algorithm 8.15 Conclusions References Chapter 9 Prediction of Compressive Strength of SCC-Containing Metakaolin and Rice Husk Ash Using Machine Learning Algorithms 9.1 Introduction 9.2 Machine Learning Approaches 9.2.1 Support Vector Machine (SVM) 9.2.1.1 Multilayer Perceptron (MLP) 9.2.1.2 Gaussian Process Regression (GPR) 9.2.1.3 Random Tree 9.2.1.4 M5P Tree 9.2.1.5 Random Forest (RF) 9.3 Methodology and Dataset 9.3.1 Dataset 9.3.2 Model Development 9.3.2.1 Supplied Test Data Method 9.3.2.2 Cross-Validation 9.4 Results and Analysis 9.5 Discussion 9.6 Conclusion References Chapter 10 Predicting Compressive Strength of Concrete Matrix Using Engineered Cementitious Composites: A Comparative Study between ANN and RF Models 10.1 Introduction 10.2 Soft Computing Techniques 10.2.1 Artificial Neural Network (ANN) 10.2.2 Random Forest (RF) 10.3 Methodology and Dataset 10.3.1 Dataset 10.3.2 Model Evaluation 10.4 Result Analysis 10.4.1 Assessment of the ANN-Based Model 10.4.2 Assessment of the RF-Based Model 10.4.3 Comparison among the Best-Developed Models 10.5 Conclusion References Chapter 11 Estimation of Marshall Stability of Asphalt Concrete Mix Using Neural Network and M5P Tree 11.1 Introduction 11.2 Machine Learning Techniques 11.2.1 Artificial Neural Network 11.2.2 M5P Model 11.3 Methodology and Dataset 11.3.1 Dataset Collection 11.3.2 Performance Assessment of Parameters 11.4 Result Analysis 11.4.1 Assessment of the ANN-Based Model 11.4.2 Assessment of the M5P Based Model 11.5 Comparison between Best Developed Models 11.6 Conclusion References Index