Sina Karimzadeh – författare

While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology.Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges.Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches.Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD.Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues.Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.

While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology.Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges.Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches.Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD.Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues.Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.

This book explores the pivotal role that smart materials and energy systems play in driving innovation and sustainability in the Fourth Industrial Revolution (4IR). The chapters cover a wide range of topics, cutting across advanced materials science, energy technologies, and the ongoing digital transformation known as the 4IR. By connecting smart materials to large-scale sustainability efforts and clean energy technologies, this work assists readers looking for solutions to climate change and global energy challenges, broadening its relevance to environmental policy and renewable energy sectors.• Examines the development, classification, and application of smart materials across key industries, emphasizing their role in driving innovation and sustainability. • Dives deeply into the evolving energy landscape and addresses the future of energy systems and clean energy solutions.• Offers authoritative insights and cutting-edge research, ensuring that readers gain access to the latest developments and trends.• Provides a future-oriented analysis of how smart materials can be applied across diverse industries such as renewable energy, nanotechnology, and smart grids.• Involves real-world examples of smart materials used in healthcare, construction, and renewable energy, helping readers understand how these innovations are applied in practice.• Emphasizes sustainability, energy efficiency, and the role of smart materials in addressing global energy challenges. Offering forward-looking insights into emerging technologies and trends in smart materials, energy storage, and clean energy, this book equips readers in materials, chemical, and related engineering disciplines with the knowledge to stay ahead in their fields and adapt to future industry shifts.