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Machine learning meets quantum physics

Schutt, Kristof T.

Machine learning meets quantum physics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning meets quantum physicsedited by Kristof T. Schutt ... [et al.].
其他作者:	Schutt, Kristof T.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xvi, 467 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-030-40245-7
ISBN:	9783030402457$q(electronic bk.)

Machine learning meets quantum physics
Machine learning meets quantum physics[electronic resource] /edited by Kristof T. Schutt ... [et al.]. - Cham :Springer International Publishing :2020. - xvi, 467 p. :ill., digital ;24 cm. - Lecture notes in physics,v.9680075-8450 ;. - Lecture notes in physics ;650..

Introduction to Material Modeling -- Kernel Methods for Quantum Chemistry -- Introduction to Neural Networks -- Building nonparametric n-body force fields using Gaussian process regression -- Machine-learning of atomic-scale properties based on physical principles -- Quantum Machine Learning with Response Operators in Chemical Compound Space -- Physical extrapolation of quantum observables by generalization with Gaussian Processes -- Message Passing Neural Networks -- Learning representations of molecules and materials with atomistic neural networks -- Molecular Dynamics with Neural Network Potentials -- High-Dimensional Neural Network Potentials for Atomistic Simulations -- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights -- Active learning and Uncertainty Estimation -- Machine Learning for Molecular Dynamics on Long Timescales -- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design -- Polymer Genome: A polymer informatics platform to accelerate polymer discovery -- Bayesian Optimization in Materials Science -- Recommender Systems for Materials Discovery -- Generative Models for Automatic Chemical Design.

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.

ISBN: 9783030402457$q(electronic bk.)

Standard No.: 10.1007/978-3-030-40245-7doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .M334 2020

Dewey Class. No.: 006.31

Machine learning meets quantum physics
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505 0 $a Introduction to Material Modeling -- Kernel Methods for Quantum Chemistry -- Introduction to Neural Networks -- Building nonparametric n-body force fields using Gaussian process regression -- Machine-learning of atomic-scale properties based on physical principles -- Quantum Machine Learning with Response Operators in Chemical Compound Space -- Physical extrapolation of quantum observables by generalization with Gaussian Processes -- Message Passing Neural Networks -- Learning representations of molecules and materials with atomistic neural networks -- Molecular Dynamics with Neural Network Potentials -- High-Dimensional Neural Network Potentials for Atomistic Simulations -- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights -- Active learning and Uncertainty Estimation -- Machine Learning for Molecular Dynamics on Long Timescales -- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design -- Polymer Genome: A polymer informatics platform to accelerate polymer discovery -- Bayesian Optimization in Materials Science -- Recommender Systems for Materials Discovery -- Generative Models for Automatic Chemical Design.
520 $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.
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