Applied Computational Materials Modeling
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Applied Computational Materials Modeling
Author: Guillermo Bozzolo
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-12-19
While it is tempting to label computational materials modeling as an emerging field of research, the truth is that both in nature and foundation, it is just as much an established field as the concepts and techniques that define it. It is the recent enormous growth in computing power and communications that has brought the activity to the forefi-ont, turning it into a possible com ponent of any modem materials research program. Together with its increased role and visibility, there is also a dynamic change in the way computational modeling is perceived in such a vast field as materials science with its wide range of length and time scales. As the pace of materials research accelerates and the need for often inaccessible information continues to grow, the de mands and expectations on existing modeling techniques have progressed that much faster. Primarily because there is no one technique that can provide all the answers at every length and time scale in materials science, excessive expectations of computational materials modeling should be avoided if pos sible. While it is apparent that computational modeling is the most efficient method for dealing with complex systems, it should not be seen as an alter native to traditional experimentation. Instead there is another option, which is perhaps the one that is most likely to become the defining characteristic of computational materials modeling.
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. - Introduces the reader to core concepts in AI and machine learning in the context of biomaterials, as well as providing practical examples to aid understanding - Thoroughly reviews the role of AI and machine learning in the synthesis, characterization, and applications of novel biomaterials - Delivers in-depth coverage of discriminative and generative models for properties prediction and de novo materials design/discovery
Advanced Computational Materials Modeling
Author: Miguel Vaz Junior
language: en
Publisher: John Wiley & Sons
Release Date: 2010-12-06
With its discussion of strategies for modeling complex materials using new numerical techniques, mainly those based on the finite element method, this monograph covers a range of topics including computational plasticity, multi-scale formulations, optimization and parameter identification, damage mechanics and nonlinear finite elements.