Computational Technologies in Materials Science

著者名：Tayal, Shubham (EDT)/Singla, Parveen (EDT)/Nandi, Ashutosh (EDT)/Davim, J. Paulo (EDT)

CRC Press（2021/10/06発売）

• 言語：ENG
• ISBN：9780367640583
• eISBN：9781000459838

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.

Table of Contents

Chapter 1 Fabrication and Characterization of Materials

[Laxman Raju Thoutam]

Chapter 2 Application to Advanced Materials Simulation

[Soumyajit Maitra, Souhardya Bera, and Subhasis Roy]

Chapter 3 Molecular Dynamics Simulations for Structural

Characterization and Property Prediction of Materials

[S. K. Joshi]

Chapter 4 Desirability Approach-Based Optimization of Process

Parameters in Turning of Aluminum Matrix Composites

[P.V. Rajesh and A. Saravanan]

Chapter 5 Spark Plasma-Induced Combustion Synthesis,

Densification, and Characterization of Nanostructured

Magnesium Silicide for Mid Temperature Energy Conversion

Energy Harvesting Application

[P. Vivekanandhan, R. Murugasami, and S. Kumaran]

Chapter 6 The Role of Computational Intelligence in Materials Science:

An Overview

[J. Ajayan, Shubham Tayal, Sandip Bhattacharya,

and L. M. I Leo Joseph]

Chapter 7 Characterization Techniques for Composites using

AI and Machine Learning Techniques

[Dr. Zeeshan Ahmad, Dr. Hasan Zakir Jafri,

and Dr. R. Hafeez Basha]

Chapter 8 Experimental Evaluation on Tribological Behavior of TiO2

Reinforced Polyamide Composites Validated by Taguchi

and Machine Learning Methods

[S. Sathees Kumar, Ch. Nithin Chakravarthy, and R. Muthalagu]

Chapter 9 Prediction of Compressive Strength of SCC-Containing

Metakaolin and Rice Husk Ash Using Machine Learning

Algorithms

[Sejal Aggarwal, Garvit Bhargava, and Parveen Sihag]

Chapter 10 Predicting Compressive Strength of Concrete Matrix Using

Engineered Cementitious Composites: A Comparative Study

between ANN and RF Models

[Nitisha Sharma, Mohindra Singh Thakur, Viola Vambol,

and Sergij Vambol]

Chapter 11 Estimation of Marshall Stability of Asphalt Concrete

Mix Using Neural Network and M5P Tree

[Ankita Upadhya, Mohindra Singh Thakur,

Siraj Muhammed Pandhian, and Shubham Tayal]