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Materials Science AAP Advances in Materials, Manufacturing and Computational Intelligence Techniques CALL FOR BOOK CHAPTERS
Short description about the volume:
In the rapidly evolving field of materials science, the integration of machine learning (ML) and artificial intelligence (AI) promises to fundamentally transform the development and analysis of nanomaterials. This edited collection will offer new insights into one of the most exciting intersections of technology and engineering, examining the groundbreaking potential of AI and ML in the discovery, design, and utilization of next-generation nanomaterials. By thoroughly investigating how machine learning and AI algorithms are reshaping nanomaterial design, the book will address a critical gap in existing literature. While previous studies have concentrated on the theoretical aspects of nanotechnology, this volume will uniquely demonstrate how innovations in mechanical engineering, particularly concerning nanomaterials, can be accelerated through computational tools.
The book will feature contributions from numerous experts, highlighting state-of-the-art techniques, practical applications, and case studies that illustrate the societal impact of AI and ML. Organized into coherent sections, it will cover a broad spectrum of topics, including the significance of predictive modelling, optimization algorithms, data-driven design, and the role of AI in the experimental discovery of nanomaterials. Each section will integrate advanced methods with industry-relevant applications, offering valuable insights for researchers, postgraduate students, and practitioners.
With its multidisciplinary approach and focus on both theoretical and practical elements, next-generation nanomaterials serve as an indispensable resource for those interested in how AI and ML will shape the future of nanomaterials engineering and innovation. This book will be essential for those at the forefront of materials science and engineering, whether for academic research, professional development, or advanced training.
CONTENTS:
Coverage
Submission of book chapters should be focused on the following key highlights:
Chapter 1. AI and ML in Nanomaterials: An Introduction to the Convergence of Two Revolutionary Technologies
(Overview of the intersection between AI, ML, and nanotechnology; Evolution from conventional materials discovery to intelligent design; Role of data-driven research in accelerating innovation; Framework of AI-assisted materials science and future opportunities.)
Chapter 2. Understanding Nanomaterials: A Foundation for AI-Driven Discovery
(Fundamental properties and classification of nanomaterials; Relationship between nanoscale structure and function; Application of AI/ML in characterizing and manipulating nanomaterials; Case studies of AI-enabled nanomaterials for energy storage and power generation.)
Chapter 3. Machine Learning Algorithms for Predictive Modelling in Nanomaterials Discovery
(Overview of supervised, unsupervised, and reinforcement learning; Predictive modeling for material properties and performance; Reducing experimental dependency through AI-based simulation; Comparative analysis of algorithmic approaches for materials prediction.)
Chapter 4. Data-Driven Design of Nanomaterials: Optimizing Performance with AI
(Integration of big data analytics with materials design; Application of optimization algorithms such as genetic algorithms and Bayesian optimization; AI-assisted multi-objective optimization of nanomaterial performance; Strategies for intelligent design of next-generation materials.)
Chapter 5. High-Throughput Screening: AI in Accelerating Nanomaterial Discovery
(Automation in materials discovery pipelines; Role of AI in high-throughput experimentation and data curation; Rapid identification of promising nanomaterial candidates; Application examples in energy, medicine, and electronics.)
Chapter 6. Integrating AI with Experimental Nanotechnology: A Hybrid Approach
(Synergistic relationship between AI modeling and laboratory experimentation; Closed-loop feedback systems for experimental validation; AI-guided synthesis and nano structuring; Case studies highlighting hybrid AI-experimental workflows.)
Chapter 7. Deep Learning and Feature Extraction in Nanomaterials Research
(Application of deep learning in materials characterization; Convolutional neural networks (CNNs) for image and spectroscopy data analysis; Feature extraction from high-dimensional datasets; Enhancing interpretability and accuracy in nanomaterial analysis.)
Chapter 8. Artificial Intelligence in Nanomaterial Simulation: Bridging the Gap Between Theory and Application
(AI-assisted simulation of nanoscale systems; Integration of quantum mechanics and machine learning models; Accelerating theoretical-to-practical transitions; Predicting structural, electronic, and mechanical behaviors using hybrid AI-physics models.)
Chapter 9. AI-Driven Nanomaterial Synthesis: Exploring New Approaches to Material Fabrication
(AI optimization in chemical vapor deposition (CVD) and atomic layer deposition (ALD); Predictive synthesis and process control; Intelligent parameter tuning for high-quality nanomaterials; Industrial applications of AI-enabled synthesis.)
Chapter 10. AI and ML for Tailoring Nanomaterials for Specific Applications
(AI-driven customization of nanomaterials for targeted uses; Predictive modeling for functional properties; Application-specific design in catalysis, drug delivery, and sensor technology; Adaptive learning for cross-sector material optimization.)
Chapter 11. AI in Sustainable Nanomaterial Design: Solving Environmental Challenges
(Integration of sustainability in AI-driven materials research; Predicting eco-friendly and energy-efficient nanomaterials; Minimization of material waste through data analytics; AI-assisted pathways toward circular and green nanomanufacturing.)
Chapter 12. Role of AI in Nanomaterial Characterization: Beyond Conventional Techniques
(Enhancing conventional characterization tools with AI; Automation of XRD, SEM, and Raman spectroscopy data analysis; Improving precision, speed, and resolution through ML models; Case examples of AI-enhanced nanoscale imaging and analytics.)
Chapter 13. Machine Learning in Nanomaterial Process Optimization
(AI-based control and optimization of nanomanufacturing processes; Reducing defects and ensuring scalability; Predictive maintenance and real-time process monitoring; Industrial applications in semiconductors and nanocomposites.)
Chapter 14. Case Studies in AI-Enhanced Nanomaterial Discovery for Energy Storage Applications
(Real-world examples of AI-assisted energy materials; Predictive performance modeling for batteries and supercapacitors; ML-driven durability and efficiency prediction; Lessons from AI-integrated research in energy systems.)
Chapter 15. AI in the Design of Nanocomposites for Advanced Manufacturing
(Role of AI in optimizing nanoparticle dispersion and interfacial bonding; Data-driven design of high-performance nanocomposites; Enhancing mechanical, electrical, and thermal properties through AI-assisted modeling; Applications in advanced manufacturing industries.)
Chapter 16. AI-Driven Nanomaterial Discovery in Medicine: From Design to Clinical Applications
(AI-assisted design of nanomaterials for biomedical applications; Predictive modeling for drug delivery and biocompatibility; Intelligent synthesis of nanostructures for diagnostics and therapy; Transition from laboratory design to clinical translation.)
Chapter 17. AI in Nanomaterials for Smart Sensors and IoT Devices
(AI-driven design of nano sensors and electronic interfaces; Machine learning algorithms for enhanced sensitivity and selectivity; Integration of smart nanomaterials into IoT ecosystems; Future directions in intelligent sensing technologies.)
Important Dates:
Abstract Submission (200 – 300 words) Deadline: December 30th, 2025
Notification of Acceptance: January 10th, 2026
Full Chapter (7,000 – 8,000 words) Submission Due: On or before March 25th, 2026
Submission procedure:
We invite researchers and practitioners to contribute original chapters to this book. Prospective authors are encouraged to submit a one-page chapter proposal or abstract outlining the proposed chapter’s content, objectives, and methodology by December 30th, 2025, including the chapter title and author details within the proposal. Authors will be notified regarding the acceptance of their proposals by January 10th, 2026. Accepted authors will be required to submit completed chapters of 15 to 20 pages by March 25th, 2026. All submitted chapters will undergo a rigorous peer-review process.
To submit your proposal or full-length chapter, please send a Word document attachment to Email: nxgnano.aap2025@gmail.com
Authors must refer to the following link for detailed guidelines for chapter preparation: http://www.appleacademicpress.com/publishwithus
Note: Authors submitting manuscripts to this book do not incur any publication fees. To ensure the originality and quality of the content, all submissions must be previously unpublished and not under consideration for publication in any other venue.
About the Authors / Editors: Editors: Dr. Abdel-Hamid Ismail MouradProfessor; Mechanical and Aerospace Engineering Department, Faculty of Engineering, United Arab Emirates University, UAE Dr. Deepen Banoriya Professor; Department of Mechanical Engineering, Faculty of Engineering & Technology, Poornima University, Jaipur, Rajasthan, India Dr. Rajan Kumar Assistant Professor, Department of Mechanical Engineering, IES Institute of Technology & Management (IES University), Bhopal, M.P., IndiaMr. Hemant Kumar Upadhyay Assistant Professor; Department of Mechanical Engineering, Faculty of Engineering & Technology, Poornima University, Jaipur, Rajasthan, India
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AAP book title: Advances in Audiology and Hearing Science (2-volume set)
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