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Machine learning in molecular sciences

Qu, Chen.

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Machine learning in molecular sciences

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning in molecular sciences/ edited by Chen Qu, Hanchao Liu.
other author:	Qu, Chen.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	x, 317 p. :ill., digital ;24 cm.
[NT 15003449]:	An Introduction to Machine Learning in Molecular Sciences -- Graph Neural Networks for Molecules -- Voxelized representations of atomic systems for machine learning applications -- Development of exchange-correlation functionals assisted by machine learning -- Machine-Learning for Static and Dynamic Electronic Structure Theory -- Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions -- Machine Learning Applications in Chemical Kinetics and Thermochemistry -- Synthesize in A Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis -- Machine Learning for Protein Engineering.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-031-37196-7
ISBN:	9783031371967

Machine learning in molecular sciences
Machine learning in molecular sciences[electronic resource] /edited by Chen Qu, Hanchao Liu. - Cham :Springer International Publishing :2023. - x, 317 p. :ill., digital ;24 cm. - Challenges and advances in computational chemistry and physics,v. 362542-4483 ;. - Challenges and advances in computational chemistry and physics ;v. 36..

An Introduction to Machine Learning in Molecular Sciences -- Graph Neural Networks for Molecules -- Voxelized representations of atomic systems for machine learning applications -- Development of exchange-correlation functionals assisted by machine learning -- Machine-Learning for Static and Dynamic Electronic Structure Theory -- Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions -- Machine Learning Applications in Chemical Kinetics and Thermochemistry -- Synthesize in A Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis -- Machine Learning for Protein Engineering.

Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.

ISBN: 9783031371967

Standard No.: 10.1007/978-3-031-37196-7doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .M33 2023

Dewey Class. No.: 006.31

Machine learning in molecular sciences
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245 0 0 $a Machine learning in molecular sciences $h [electronic resource] / $c edited by Chen Qu, Hanchao Liu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a x, 317 p. : $b ill., digital ; $c 24 cm.
490 1 $a Challenges and advances in computational chemistry and physics, $x 2542-4483 ; $v v. 36
505 0 $a An Introduction to Machine Learning in Molecular Sciences -- Graph Neural Networks for Molecules -- Voxelized representations of atomic systems for machine learning applications -- Development of exchange-correlation functionals assisted by machine learning -- Machine-Learning for Static and Dynamic Electronic Structure Theory -- Data Quality, Data Sampling and Data Fitting: A Tutorial Guide for Constructing Full-dimensional Accurate Potential Energy Surfaces (PESs) of Molecules and Reactions -- Machine Learning Applications in Chemical Kinetics and Thermochemistry -- Synthesize in A Smart Way: A Brief Introduction to Intelligence and Automation in Organic Synthesis -- Machine Learning for Protein Engineering.
520 $a Machine learning and artificial intelligence have propelled research across various molecular science disciplines thanks to the rapid progress in computing hardware, algorithms, and data accumulation. This book presents recent machine learning applications in the broad research field of molecular sciences. Written by an international group of renowned experts, this edited volume covers both the machine learning methodologies and state-of-the-art machine learning applications in a wide range of topics in molecular sciences, from electronic structure theory to nuclear dynamics of small molecules, to the design and synthesis of large organic and biological molecules. This book is a valuable resource for researchers and students interested in applying machine learning in the research of molecular sciences.
650 0 $a Machine learning. $3 533906
650 0 $a Molecular biology $x Data processing. $3 553672
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Molecular Modelling. $3 3661500
650 2 4 $a Theoretical Chemistry. $3 3591855
650 2 4 $a Computational Chemistry. $3 3595554
650 2 4 $a Computational and Systems Biology. $3 3531279
700 1 $a Qu, Chen. $3 3666023
700 1 $a Liu, Hanchao. $3 3426648
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Challenges and advances in computational chemistry and physics ; $v v. 36. $3 3666024
856 4 0 $u https://doi.org/10.1007/978-3-031-37196-7
950 $a Chemistry and Materials Science (SpringerNature-11644)