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Scientific discovery is entering a new era-one where artificial intelligence, computational physics, and intelligent optimization work together to accelerate the search for materials that could redefine modern technology.
Quantum Material Discovery - Volume II transforms the scientific foundations established in Volume I into a complete computational discovery workflow. From Bayesian optimization and active learning to phonon stability, convex hull analysis, synthesizability, and AI-guided candidate selection, this volume demonstrates how modern computational methods dramatically reduce the search space for next-generation quantum materials.
Through real-world case studies, computational validation strategies, and an integrated discovery pipeline, readers gain practical insight into how machine learning, first-principles simulation, and quantum-inspired optimization can be combined to identify promising superconducting materials for future experimental investigation.
Designed for graduate students, researchers, computational physicists, materials scientists, AI practitioners, and engineers, this volume presents a rigorous roadmap for one of the fastest-growing frontiers in scientific computing: AI-driven quantum materials discovery.
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Take 20% off your first order
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