TY  - BOOK
AU  - Sukumar,N.
ED  - SpringerLink (Online service)
TI  - Navigating Molecular Networks
T2  - SpringerBriefs in Materials,
SN  - 9783031762901
AV  - QC174.7-175.36 
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Statistical Physics
KW  - Biophysics
KW  - Biomolecules
KW  - Graph theory
KW  - Stochastic processes
KW  - Machine learning
KW  - Soft condensed matter
KW  - Molecular Biophysics
KW  - Graph Theory
KW  - Stochastic Networks
KW  - Machine Learning
KW  - Soft Materials
N1  - Molecular Networks -- Transformations of Chemical Space -- Spectral Graph Theory -- Universality and Random Matrix Theory -- Mapping and Navigating Chemical Space Networks -- Generative AI – Growing the Network -- Discovery and Creativity
N2  - 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 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
UR  - https://doi.org/10.1007/978-3-031-76290-1
ER  -