Books > Science & Mathematics > Physics > Quantum physics (quantum mechanics)
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Machine Learning Meets Quantum Physics (Paperback, 1st ed. 2020)
Loot Price: R2,731
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Machine Learning Meets Quantum Physics (Paperback, 1st ed. 2020)
Series: Lecture Notes in Physics, 968
Expected to ship within 10 - 15 working days
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Designing molecules and materials with desired properties is an
important prerequisite for advancing technology in our modern
societies. This requires both the ability to calculate accurate
microscopic properties, such as energies, forces and electrostatic
multipoles of specific configurations, as well as efficient
sampling of potential energy surfaces to obtain corresponding
macroscopic properties. Tools that can provide this are accurate
first-principles calculations rooted in quantum mechanics, and
statistical mechanics, respectively. Unfortunately, they come at a
high computational cost that prohibits calculations for large
systems and long time-scales, thus presenting a severe bottleneck
both for searching the vast chemical compound space and the
stupendously many dynamical configurations that a molecule can
assume. To overcome this challenge, recently there have been
increased efforts to accelerate quantum simulations with machine
learning (ML). This emerging interdisciplinary community
encompasses chemists, material scientists, physicists,
mathematicians and computer scientists, joining forces to
contribute to the exciting hot topic of progressing machine
learning and AI for molecules and materials. The book that has
emerged from a series of workshops provides a snapshot of this
rapidly developing field. It contains tutorial material explaining
the relevant foundations needed in chemistry, physics as well as
machine learning to give an easy starting point for interested
readers. In addition, a number of research papers defining the
current state-of-the-art are included. The book has five parts
(Fundamentals, Incorporating Prior Knowledge, Deep Learning of
Atomistic Representations, Atomistic Simulations and Discovery and
Design), each prefaced by editorial commentary that puts the
respective parts into a broader scientific context.
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