000			01744nam a22002057a 4500
005			20250515152032.0
008			250507b |||||||| |||| 00| 0 eng d
020			_a9783031762895
028			_aSamyak Vidyasagar _d15/05/2025 _cBOS-000489 _b; Ghaziabad Uttar Pradesh-201012
082			_a572.80 SUK
100			_aN Sukumar
245			_aNavigating Molecular Networks _c\N Sukumar
260			_aSwitzerland _b:Springer Nature _c,2025
300			_a114 Pages _c;23 cm
365			_b3524
520			_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.
650			_aPhysics
655			_a;Molecular Networks
942			_cBOOK
999			_c63000 _d63000