Machine learning in chemistry : the impact of artificial intelligence

This book provides practical examples of machine learning applied to science to help researchers make an informed choice about using the method in chemistry.

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Bibliographic Details
Other Authors: Cartwright, Hugh M., 1948- (Editor)
Format: eBook
Language: English
Published: London : Royal Society of Chemistry, 2020.
Series: Theoretical and computational chemistry ; 17.
Subjects:
Chemistry > Data processing.
Machine learning.
elektronické knihy
electronic books
ISBN: 9781839160233
1839160233
9781839160240
1839160241
9781788017893
1788017897
Physical Description: 1 online resource

Cover

Table of contents

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035 |a (OCoLC)1179002329  |z (OCoLC)1190850717 
245 0 0 |a Machine learning in chemistry :  |b the impact of artificial intelligence /  |c edited by Hugh M. Cartwright. 
264 1 |a London :  |b Royal Society of Chemistry,  |c 2020. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Theoretical and computational chemistry ;  |v 17 
504 |a Includes bibliographical references and index. 
505 0 |a Computers as scientists -- How do machines learn? -- MedChemInformatics: an introduction to machine learning for drug discovery -- Machine learning for nonadiabatic molecular dynamics -- Machine learning in science-a role for mechanic sympathy? -- A prediction of future states: AI-powered chemical innovation for defense applications -- Machine learning for chemical synthesis -- Constraining chemical networks in astrochemistry -- Machine learning at he (nano)materials-biology interface -- Machine learning techniques applied to a complex polymerization process -- machine learning and scoring functions (SFs) for molecular drug discovery: prediction and characterisation of druggable drugs and targets -- Artificial intelligence applied to the prediction of organic materials -- A new era of inorganic materials discovery powered by data science -- Machine learning application sin chemical engineering -- representation learning in chemistry -- Demystifying artificial neural networks as generators of new chemical knowledge: antimalarial drug discovery as a case study -- Machine learning for core-loss spectrum -- Autonomous science: big data tools for small data problems in chemistry -- Machine learning for heterogeneous catalysis: global neural network potential from construction to applications -- A few guiding principles for practical applications of machine learning to chemistry and materials. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a This book provides practical examples of machine learning applied to science to help researchers make an informed choice about using the method in chemistry. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Chemistry  |x Data processing. 
650 0 |a Machine learning. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Cartwright, Hugh M.,  |d 1948-  |e editor.  |1 https://id.oclc.org/worldcat/entity/E39PCjCDf8qmw4p7hTG3pqD7BP 
776 0 8 |i Print version:  |t Machine learning in chemistry.  |d Cambridge : Royal Society of Chemistry, 2020  |z 9781788017893  |w (OCoLC)1173575313 
830 0 |a Theoretical and computational chemistry ;  |v 17. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpMLCTIAI1/machine-learning-in?kpromoter=marc  |y Full text 

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