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000937222 019__ $$a1162193179$$a1162842623$$a1163592608$$a1163809227$$a1164674796
000937222 020__ $$a9783030402457$$q(electronic book)
000937222 020__ $$a3030402452$$q(electronic book)
000937222 020__ $$z9783030402440
000937222 020__ $$z3030402444
000937222 0247_ $$a10.1007/978-3-030-40
000937222 035__ $$aSP(OCoLC)on1157089570
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000937222 050_4 $$aQ325.5
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000937222 24500 $$aMachine learning meets quantum physics /$$cKristof T. Schütt [and more], editors.
000937222 260__ $$aCham :$$bSpringer,$$c2020.
000937222 300__ $$a1 online resource (473 pages).
000937222 336__ $$atext$$btxt$$2rdacontent
000937222 337__ $$acomputer$$bc$$2rdamedia
000937222 338__ $$aonline resource$$bcr$$2rdacarrier
000937222 4901_ $$aLecture Notes in Physics Ser. ;$$vv.968
000937222 504__ $$aIncludes bibliographical references.
000937222 506__ $$aAccess limited to authorized users.
000937222 520__ $$aDesigning 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.
000937222 588__ $$aDescription based on print version record.
000937222 650_0 $$aMachine learning.
000937222 650_0 $$aQuantum theory.
000937222 7001_ $$aSchütt, Kristof T.
000937222 7001_ $$aChmiela, Stefan.
000937222 7001_ $$avon Lilienfeld, O. Anatole.
000937222 7001_ $$aTkatchenko, Alexandre.
000937222 7001_ $$aTsuda, Koji.
000937222 7001_ $$aMüller, Klaus-Robert.
000937222 77608 $$iPrint version:$$aSchütt, Kristof T.$$tMachine Learning Meets Quantum Physics$$dCham : Springer International Publishing AG,c2020$$z9783030402440
000937222 830_0 $$aLecture notes in physics ;$$v968.
000937222 852__ $$bebk
000937222 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-40245-7$$zOnline Access$$91397441.1
000937222 909CO $$ooai:library.usi.edu:937222$$pGLOBAL_SET
000937222 980__ $$aEBOOK
000937222 980__ $$aBIB
000937222 982__ $$aEbook
000937222 983__ $$aOnline
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