Navigating Molecular Networks

I am delighted to announce my forthcoming book Navigating Molecular Networks: Exploring the Chemical Space Concept in Novel Materials Design as part of the SpringerBriefs in Materials book series. The book

1. Caters to a diverse audience encompassing students and researchers in physics, chemistry, and materials science;
2. Incorporates a multipronged approach spanning from vector space analysis to random matrix theory;
3. Explores graph and deep learning applications in molecular and materials design.

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.

Softcover ISBN: 978-3-031-76289-5
eBook ISBN: 978-3-031-76290-1

Table of contents
Chapter 1: Molecular networks

1. Why Molecular Networks? Graphs and Simplices
2. Matrix representations of Weighted and Unweighted Networks
3. Matrix representations of Directed and Undirected Graphs
4. Unipartite and Bipartite Networks
5. Coordinate and Graph representations of Chemical Space
6. Feature Networks

Chapter 2: Transformations of Chemical space

1. Vector spaces and Metric Tensors
2. Dimensionality reduction
3. Similarity Kernels and Kernel methods

Chapter 3: Spectral Graph Theory

1. Network measures
2. Eigenvalues of the Adjacency Matrix
3. Eigenvalues of the Laplacian Matrix
4. Graph Centrality measures
5. Graph Curvature
6. Eigenvectors of the modularity matrix

Chapter 4: Universality and Random Matrix theory

1. Eigenvalue correlations
2. RMT for Chemical Reaction Networks
3. RMT for Feature Networks

Chapter 5: Mapping and Navigating Chemical Space Networks

1. k-NN and k-Means
2. Visualizing Chemical Space Networks
3. Model Applicability Domain and Scaffold Hopping
4. Violation of the Similarity principle - Activity Cliffs

Chapter 6: Generative AI – Growing the Network

1. Genetic Algorithms
2. Back propagation and Variational Auto Encoders
3. Graph Convolutional Networks
4. Generative Adversarial Networks and Reinforcement Learning
5. Transformers and Generative Language Models
6. Why does Over-parametrization work?
7. Infinitely wide networks and Neural tangent kernels
8. Extensions and Future Directions

Chapter 7: Discovery and Creativity
Glossary of terms