Navigating Molecular Networks
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Beschreibung
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.
Caters to a diverse audience encompassing students and researchers in physics, chemistry, and materials science Incorporates a multipronged approach spanning from vector space analysis to random matrix theory Explores graph and deep learning applications in molecular and materials design
Autor*in
N. Sukumar
Themen in »Navigating Molecular Networks«
Generative Deep Learning for Materials Design Topology of Molecular Networks Chemical Space Exploration Spectral Graph Theory Molecular Similarity Kernels