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9,50 €Step into the future of science where artificial intelligence meets quantum chemistry.
In Contemporary Advances in Artificial Intelligence Applications to Theoretical and Computational Chemistry, Nohil Kodiyatar offers an in-depth and cutting-edge exploration of how artificial intelligence (AI) is revolutionizing the foundations and frontiers of theoretical chemistry. Merging quantum mechanics, machine learning, and computational modeling, this book provides a comprehensive and scholarly guide to the most transformative developments shaping molecular science today.
This first-of-its-kind volume introduces readers to the fundamental principles of quantum chemistry—such as solving the Schrödinger equation, wavefunction modeling, and potential energy surface (PES) analysis—and then shows how modern AI tools are drastically enhancing the speed, scalability, and accuracy of these traditionally complex computations.
You will discover:
• The integration of AI techniques like deep neural networks, Gaussian processes, reinforcement learning, and graph neural networks into quantum chemistry workflows.
• Innovative architectures like DeepMind’s FermiNet and PauliNet, capable of directly learning antisymmetric wavefunctions with unprecedented precision.
• The role of OrbNet, delta-learning, and ML-augmented DFT in predicting molecular properties, reaction mechanisms, and energy calculations across diverse chemical spaces.
• Applications in AI-assisted spectroscopy, drug discovery, materials science, and multiscale modeling—from atomistic to macroscopic levels.
• Challenges in data scarcity, model interpretability, generalization, and computational sustainability.
• Visionary directions toward AI-complete automation, quantum machine learning, and autonomous scientific discovery platforms.
Richly referenced and logically structured, this book serves not only as an advanced academic reference but also as a roadmap for future research and innovation at the intersection of chemistry, artificial intelligence, and quantum technology.
Whether you're a computational chemist, quantum physicist, AI researcher, materials scientist, or a graduate student aiming to understand the future of scientific computing—this book is your essential guide to the rapidly evolving field of AI-driven theoretical chemistry.
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