01691nam a22001817a 450000500170000000800410001702000180005802800810007608200150015710000140017224500460018626000410023230000220027336500090029552011690030465000120147365500240148520250515152032.0250507b        |||||||| |||| 00| 0 eng d  a9783031762895  aSamyak Vidyasagard15/05/2025cBOS-000489b; Ghaziabad Uttar Pradesh-201012   a572.80 SUK  aN Sukumar  aNavigating Molecular Networksc\N Sukumar  aSwitzerlandb:Springer Naturec,2025  a114 Pagesc;23 cm  b3524  aThis 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.
  aPhysics  a;Molecular Networks