Download Navigating Molecular Networks: Exploring the Chemical Space Concept in Novel Materials Design
Navigating Molecular Networks: exploring the chemical space concept in novel materials design pdf download. This book delves into the foundational principles governing the treatment of molecular networks and “chemical space”―the comprehensive domain encompassing all physically achievable molecules―from the perspectives of vector space, graph theory, and data science. It explores similarity kernels, network measures, spectral graph theory, and random matrix theory, weaving intriguing connections between these diverse subjects. Notably, it emphasizes the visualization of molecular networks.
The book is addressed at senior undergraduate and graduate students in physics, chemistry, and materials science, as well as anyone interested in learning about how computational, network, AI and ML techniques are poised to transform the search for new materials. This book explores the theoretical principles underlying the treatment of molecular networks and “chemical space”, the abstract space of all possible physically realizable molecules, from vector space, graph theoretic and data science perspectives, bringing out the tantalizing threads of connections between diverse topics such as Similarity Kernels, Gaussian Processes, Network measures, Spectral Graph Theory and Random Matrix theory. Visualization of molecular networks and navigating these networks also receive due attention.
This is followed up with an exploration of modern Generative Deep Learning models that are being increasingly used in the search for new materials with specific properties, and some of the most exciting developments in this area. The book closes with a discussion on the meanings of discovery and creativity, and the role of artificial intelligence (AI) therein. The exploration continues by delving into contemporary generative deep learning models, increasingly pivotal in the pursuit of new materials possessing specific properties, showcasing some of the most compelling advancements in this field. Concluding with a discussion on the meanings of discovery, creativity, and the role of artificial intelligence (AI) therein. Its primary audience comprises senior undergraduate and graduate students specializing in physics, chemistry, and materials science. Additionally, it caters to those interested in the potential transformation of material discovery through computational, network, AI, and machine learning (ML) methodologies.
eBook details
- Author (s): N. Sukumar
- Year of publication: 2025
- Publisher: Springer
- Language: English
- ISBN: 3031762894, 9783031762895, 9783031762901
- Number of pages: 124 pages
- Book format: PDF
- File size: 3 MB
