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Full Description
Advanced materials are essential for economic security and human well-being, with applications in industries aimed at addressing challenges in clean energy, national security, and human welfare. Yet, it can take years to move a material to the market after its initial discovery. Computational techniques have accelerated the exploration and development of materials, offering the chance to move new materials to the market quickly. Computational Technologies in Materials Science addresses topics related to AI, machine learning, deep learning, and cloud computing in materials science. It explores characterization and fabrication of materials, machine-learning-based models, and computational intelligence for the synthesis and identification of materials. This book
• Covers material testing and development using computational intelligence
• Highlights the technologies to integrate computational intelligence and materials science
• Details case studies and detailed applications
• Investigates challenges in developing and using computational intelligence in materials science
• Analyzes historic changes that are taking place in designing materials.
This book encourages material researchers and academics to develop novel theories and sustainable computational techniques and explores the potential for computational intelligence to replace traditional materials research.
Contents
Chapter 1 Fabrication and Characterization of Materials Chapter 2 Application to Advanced Materials Simulation Chapter 3 Molecular Dynamics Simulations for Structural Characterization and Property Prediction of Materials Chapter 4 Desirability Approach-Based Optimization of Process Parameters in Turning of Aluminum Matrix Composites Chapter 5 Spark Plasma-Induced Combustion Synthesis, Densification, and Characterization of Nanostructured Magnesium Silicide for Mid Temperature Energy Conversion Energy Harvesting Application Chapter 6 The Role of Computational Intelligence in Materials Science: An Overview Chapter 7 Characterization Techniques for Composites using AI and Machine Learning Techniques Chapter 8 Experimental Evaluation on Tribological Behavior of TiO2 Reinforced Polyamide Composites Validated by Taguchi and Machine Learning Methods Chapter 9 Prediction of Compressive Strength of SCC-Containing Metakaolin and Rice Husk Ash Using Machine Learning Algorithms Chapter 10 Predicting Compressive Strength of Concrete Matrix Using Engineered Cementitious Composites: A Comparative Study between ANN and RF Models Chapter 11 Estimation of Marshall Stability of Asphalt Concrete Mix Using Neural Network and M5P Tree
