Artificial Intelligence in Biomaterials Design and Development
Artificial Intelligence in Biomaterials Design and Development delves into the transformative role of artificial intelligence, particularly machine learning, in creating new biomaterials. Traditional challenges in this field, such as chemical waste, spatial constraints, and inadequate tools, have hindered the swift design and synthesis of versatile biomaterials. Machine learning methods address these barriers by enhancing discovery and development processes, reducing time, costs, and wastage. Generative models now enable the creation of novel molecular structures with desired properties, making inverse materials design a reality. This book is essential for those in materials science, machine learning, and biomedical engineering.

Additionally, this comprehensive resource explores the application of AI in various aspects of biomaterials science, from computational engineering to data science. The book provides insights into how novel machine learning models can expedite materials discovery and improve accuracy. It is an invaluable guide for academics and industry professionals alike, seeking to leverage AI for innovative biomaterials research and development.

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Artificial Intelligence in Biomaterials Design and Development
Artificial Intelligence in Biomaterials Design and Development delves into the transformative role of artificial intelligence, particularly machine learning, in creating new biomaterials. Traditional challenges in this field, such as chemical waste, spatial constraints, and inadequate tools, have hindered the swift design and synthesis of versatile biomaterials. Machine learning methods address these barriers by enhancing discovery and development processes, reducing time, costs, and wastage. Generative models now enable the creation of novel molecular structures with desired properties, making inverse materials design a reality. This book is essential for those in materials science, machine learning, and biomedical engineering.

Additionally, this comprehensive resource explores the application of AI in various aspects of biomaterials science, from computational engineering to data science. The book provides insights into how novel machine learning models can expedite materials discovery and improve accuracy. It is an invaluable guide for academics and industry professionals alike, seeking to leverage AI for innovative biomaterials research and development.

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Artificial Intelligence in Biomaterials Design and Development

Artificial Intelligence in Biomaterials Design and Development

Artificial Intelligence in Biomaterials Design and Development

Artificial Intelligence in Biomaterials Design and Development

Overview

Artificial Intelligence in Biomaterials Design and Development delves into the transformative role of artificial intelligence, particularly machine learning, in creating new biomaterials. Traditional challenges in this field, such as chemical waste, spatial constraints, and inadequate tools, have hindered the swift design and synthesis of versatile biomaterials. Machine learning methods address these barriers by enhancing discovery and development processes, reducing time, costs, and wastage. Generative models now enable the creation of novel molecular structures with desired properties, making inverse materials design a reality. This book is essential for those in materials science, machine learning, and biomedical engineering.

Additionally, this comprehensive resource explores the application of AI in various aspects of biomaterials science, from computational engineering to data science. The book provides insights into how novel machine learning models can expedite materials discovery and improve accuracy. It is an invaluable guide for academics and industry professionals alike, seeking to leverage AI for innovative biomaterials research and development.

Product Details

ISBN-13: 9780323954648
Publisher: Elsevier Science
Publication date: 08/01/2025
Series: Woodhead Publishing Series in Biomaterials
Pages: 500
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Mohsen Khodadadi Yazdi is an experienced chemical/polymer engineer who received his BSc degree (chemical engineering) from the Ferdowsi University of Mashhad, Iran, followed by a MSc (2010) from the Sharif University of Technology, Tehran, Iran. He received his PhD (2017) in polymer engineering from the University of Tehran, Iran. He is currently a Senior Researcher at the Center of Excellence in Electrochemistry, University of Tehran, Iran. His research interests include smart hydrogel/biopolymers, carbon-based nanomaterial, conducting polymers, and machine learning for utilization in biomedical applications. He has contributed to >20 scientific papers and book chapters.

Payam Zarrintaj is an experienced polymer engineer; he received his BSc degree from Amirkabir University of Technology, Iran, followed by a MSc (2013) and PhD (2018) from Tehran University, Iran. He is currently a postdoctoral research fellow at the School of Chemical Engineering, Oklahoma State University, USA. His research interests include smart hydrogel/polymers and nanoparticles with well-controlled microstructures and properties for biomedical applications. He has contributed to >90 scientific papers and book chapters.

Dr. Mohammad Reza Saeb received his PhD in 2008 from Amirkabir University of Technology (Iran) and is currently a Professor at the Department of Pharmaceutical Chemistry, Medical University of Gdańsk (Poland). His research focuses on advanced materials and manufacturing processes, including polymer blends, composites, and nanocomposites, with particular emphasis on biomaterials and flame-retardant polymers, as well as the recycling and upcycling of polymer and biowastes. He has authored or co-authored more than 500 articles in high-impact journals and is currently serving as the Editor-in-Chief of Polymers from Renewable Resources, published by SAGE.



Dr. Masoud Mozafari is a Fellow at Lunenfeld Tanenbaum Research Institute, Mount Sinai Health Hospital, University of Toronto. He was previously Assistant Professor and Director of the Bioengineering Lab, at the Nanotechnology and Advanced Materials Department, Materials and Energy Research Center, Cellular and Molecular Research Center, and Department of Tissue Engineering and Regenerative Medicine of the Iran University of Medical Sciences (IUMS), Tehran, Iran. Dr. Mozafari’s research interests range across biomaterials, nanotechnology, and tissue engineering, and he is known for the development of strategies for the treatment of damaged tissues and organs, and controlling biological substances for targeted delivery into the human body. Dr. Mozafari has received several awards, including the Khwarizmi Award and the Julia Polak European Doctorate Award for outstanding translational research contributions to the field of biomaterials. He has also received the WIPO Medal for Inventors from The World Intellectual Property Organization (WIPO), in recognition of his contributions to economic and technological development. Dr. Mozafari is currently working on the editorial board of several journals.

Dr. Sidi A. Bencherif earned a Ph.D. in Chemistry from Carnegie Mellon University in 2009 and is a Senior Researcher at CNRS. A world-renowned expert in biomaterials, he is recognized among the top 2% of scientists worldwide by Stanford University and Elsevier. His research focuses on the design of advanced biomaterials for therapeutic applications, with an emphasis on regenerative medicine, drug delivery, and cancer immunotherapy. Dr. Bencherif’s work bridges cutting-edge materials science with clinical outcomes, addressing key medical challenges by integrating innovative biomaterials into clinical practice.

Table of Contents

1. Introduction to artificial intelligence, machine learning, and deep learning
2. Useful tools and datasets for materials science and engineering
3. Artificial neural networks
4. From human genome to materials genome
5. Biomaterials properties-prediction based on discriminative models
6. de novo materials design based on generative models
7. AI-assisted synthesis planning and optimization of biomaterials
8. AI-assisted characterization of biomaterials
9. AI-assisted evaluation of biomaterials
10. AI and biomaterials in drug and vaccine development
11. AI and biomaterials in protein engineering
12. AI in biopolymer design/discovery/engineering
13. AI in designing/discovery of other biomaterials
14. AI-assisted biomaterials structures at scales
15. AI-assisted materials/scientific discoveries: Beyond pure machine learning
16. State-of-the-art and future perspectives on ML-assisted biomaterials design/discovery

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