000			06233cam0a2200481 4500
001			16039
009			258698640
003			http://www.sudoc.fr/258698640
005			20250630092555.0
010			_a9783030402440 _bbr.
073		1	_a9783030402440
090			_a16039
099			_tOUVR _zALEX31916
100			_a20211129h20202020u y0frey50 ba
101	0		_aeng _2639-2
102			_aCH
105			_aa a 000|y
106			_ar
181			_6z01 _ctxt _2rdacontent
181		1	_6z01 _ai# _bxxxe##
182			_6z01 _cn _2rdamedia
182		1	_6z01 _an
183		1	_6z01 _anga _2RDAfrCarrier
200	0		_aMachine learning meets quantum physics _fKristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, Klaus-Robert Müller, editors
214		0	_aCham (Switzerland) _cSpringer _d[2020]
215			_a1 vol. (467 p.) _cill. _d24 cm
225	0		_aLecture notes in physics _x0075-8450 _v968
310			_aCurrent copyright fee: GBP19.00 - 42\0
320			_aNotes bibliogr.
327	1		_aIntroduction / Kristof T. Schütt, Stefan Chmiela, O. Anatole von Lilienfeld, Alexandre Tkatchenko, Koji Tsuda, and Klaus-Robert Müller _apart 1. Fundamentals. Introduction to material modeling / Jan Hermann _aKernel methods for quantum chemistry / Wiktor Pronobis and Klaus-Robert Müller _aIntroduction to neural networks / Grégor Montavon _apart 2. Incorporating prior knowledge: invariances, symmetries, conservation laws. Building nonparametric n-body force fields using Gaussian process regression / Aldo Glielmo, Claudio Zeni Ádám Fekete, and Alessandro De Vita _aMachine-learning of atomic-scale properties based on physical principles / Gábor Csányi, Michael J. Willatt, and Michele Ceriotti _aAccurate molecular dynamics enabled by efficient physically constrained machine learning approaches / Stefan Chmiela, Huziel E. Sauceda, Alexandre Tkatchenko, and Klaus-Robert Müller _aQuantum machine learning with resposne operators in chemical compound space / Felix Andreas Faber, Anders S. Christensen, and O. Anatole von Lilienfield _aPhysical extrapolation of quantum observables by generalization with Gaussian processes / R.A. Vargas-Hernández and R.V. Krems _apart 3. Deep learning of atomistic representations. Message passing neural networks / Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl _aLearning representations of molecules and materials with atomistic neural networks / Kristof T. Schütt, Alexandre Tkatchenko, and Klaus-Robert Müller _apart 4. Atomistic simulations. Molecular dynamics with neural network potentials / Michael Gastegger and Philipp Marquetand _aHigh-dimensional neural network potentials for atomistic simulations / Matti Hellström and Jörg Behler _aConstruction of machine learned force fields with quantum chemical accuracy: applications and chemical insights / Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, and Alexandre Tkatchenko _aActive learning and uncertainty estimation / Alexander Shapeev, Konstantin Gubaev, Evgenii Tsymbalov, and Evgeny Podryabinkin _aMachine learning for molecular dynamics on long timescales / Frank Noé _apart 5. Discovery and design. Database-driven high-throughput calculations and machine learning models for materials design / Rickard Armiento _aPolymer genome: a polymer informatics platform to accelerate polymer discovery / Anand Chandrasekaran, Chiho Kim, and Rampi Ramprasad _aBayesian optimization in materials science / Zhufeng Hou and Koji Tsuda _aRecommender systems for materials discovery / Atsuto Seko, Hiroyuki Hayashi, Hisashi Kashima, and Isao Tanaka _aGenerative models for automatic chemical design / Daniel Schwalbe-Koda and Rafael Gómez-Bombarelli
330			_a"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." _2éditeur
452			_tMachine learning meets quantum physics. _cCham : Springer, 2020
606			_aMachine learning _2lc
606			_aQuantum theory _2lc
676			_a530.12
680			_aQ325.5 _b.M3225 2020
702		1	_aSchütt _bKristof T. _4340
702		1	_aChmiela _bStefan _4340
702		1	_aLilienfeld _bO. Anatole von _f1976- _4340
702		1	_aTkatchenko _bA. _gAlexandre _4340
702		1	_aTsuda _bKoji _4340
702		1	_aMüller _bKlaus-Robert _4340
856			_uhttp://link.springer.com/openurl?genre=book&amp;isbn=978-3-030-40245-7 _zLivre &eacute;lectronique<br /