Data Science in Chemistry : : Artificial Intelligence, Big Data, Chemometrics and Quantum Computing with Jupyter /

Data Science in Chemistry : : Artificial Intelligence, Big Data, Chemometrics and Quantum Computing with Jupyter / / Thorsten Gressling.

The ever-growing wealth of information has led to the emergence of a fourth paradigm of science. This new field of activity – data science – includes computer science, mathematics and a given specialist domain. This book focuses on chemistry, explaining how to use data science for deep insights and...

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Bibliographic Details
Superior document:Title is part of eBook package: De Gruyter DG Ebook Package English 2021
VerfasserIn:
Gressling, Thorsten,
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2020]
©2021
Year of Publication:2020
Language:English
Series:De Gruyter Textbook
Online Access:
Physical Description:1 online resource (XVIII, 522 p.)
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Table of Contents:
  • Frontmatter
  • Preface
  • Contents
  • Introduction
  • Technical setup and naming conventions
  • 1 Data science: introduction
  • 2 Data science: the “fourth paradigm” of science
  • 3 Relations to other domains and cheminformatics
  • Part A: IT, data science, and AI
  • IT basics (cloud, REST, edge)
  • 4 Cheminformatics application landscape
  • 5 Cloud, fog, and AI runtime environments
  • 6 DevOps, DataOps, and MLOps
  • 7 High-performance computing (HPC) and cluster
  • 8 REST and MQTT
  • 9 Edge devices and IoT
  • Programming
  • 10 Python and other programming languages
  • 11 Python standard libraries and Conda
  • 12 IDE’s and workflows
  • 13 Jupyter notebooks
  • 14 Working with notebooks and extensions
  • 15 Notebooks and Python
  • 16 Versioning code and Jupyter notebooks
  • 17 Integration of Knime and Excel
  • Data engineering
  • 18 Big data
  • 19 Jupyter and Spark
  • 20 Files: structure representations
  • 21 Files: other formats
  • 22 Data retrieval and processing: ETL
  • 23 Data pipelines
  • 24 Data ingestion: online data sources
  • 25 Designing databases
  • 26 Data science workflow and chemical descriptors
  • Data science as field of activity
  • 27 Community and competitions
  • 28 Data science libraries
  • 29 Deep learning libraries
  • 30 ML model sources and marketplaces
  • 31 Model metrics: MLFlow and Ludwig
  • Introduction to ML and AI
  • 32 First generation (logic and symbols)
  • 33 Second generation (shallow models)
  • 34 Second generation: regression
  • 35 Decision trees
  • 36 Second generation: classification
  • 37 Second generation: clustering and dimensionality reduction
  • 38 Third generation: deep learning models (ANN)
  • 39 Third generation: SNN – spiking neural networks
  • 40 xAI: eXplainable AI
  • Part B: Jupyter in cheminformatics
  • Physical chemistry
  • 41 Crystallographic data
  • 42 Crystallographic calculations
  • 43 Chemical kinetics and thermochemistry
  • 44 Reaction paths and mixtures
  • 45 The periodic table of elements
  • 46 Applied thermodynamics
  • Material science
  • 47 Material informatics
  • 48 Molecular dynamics workflows
  • 49 Molecular mechanics
  • 50 VASP
  • 51 Gaussian (ASE)
  • 52 GROMACS
  • 53 AMBER, NAMD, and LAMMPS
  • 54 Featurize materials
  • 55 ASE and NWChem
  • Organic chemistry
  • 56 Visualization
  • 57 Molecules handling and normalization
  • 58 Features and 2D descriptors (of carbon compounds)
  • 59 Working with molecules and reactions
  • 60 Fingerprint descriptors (1D)
  • 61 Similarities
  • Engineering, laboratory, and production
  • 62 Laboratory: SILA and AnIML
  • 63 Laboratory: LIMS and daily calculations
  • 64 Laboratory: robotics and cognitive assistance
  • 65 Chemical engineering
  • 66 Reactors, process flow, and systems analysis
  • 67 Production: PLC and OPC/UA
  • 68 Production: predictive maintenance
  • Part C: Data science
  • Data engineering in analytic chemistry
  • 69 Titration and calorimetry
  • 70 NMR
  • 71 X-ray-based characterization: XAS, XRD, and EDX
  • 72 Mass spectroscopy
  • 73 TGA, DTG
  • 74 IR and Raman spectroscopy
  • 75 AFM and thermogram analysis
  • 76 Gas chromatography-mass spectrometry (GC-MS)
  • Applied data science and chemometrics
  • 77 SVD chemometrics example
  • 78 Principal component analysis (PCA)
  • 79 QSAR: quantitative structure–activity relationship
  • 80 DeepChem: binding affinity
  • 81 Stoichiometry and reaction balancing
  • Applied artificial intelligence
  • 82 ML Python libraries in chemistry
  • 83 AI in drug design
  • 84 Automated machine learning
  • 85 Retrosynthesis and reaction prediction
  • 86 ChemML
  • 87 AI in material design
  • Knowledge and information
  • 88 Ontologies and inferencing
  • 89 Analyzing networks
  • 90 Knowledge ingestion: labeling and optical recognition
  • 91 Content mining and knowledge graphs
  • Part D: Quantum computing and chemistry Introduction
  • 92 Quantum concepts
  • 93 QComp: technology vendors
  • 94 Quantum computing simulators
  • 95 Quantum algorithms
  • 96 Quantum chemistry software (QChem)
  • Quantum Computing Applications
  • 97 Application examples
  • 98 Simulating molecules using VQE
  • 99 Studies on small clusters of LiH, BeH2, and NaH
  • 100 Quantum machine learning (QAI)
  • Code index
  • Index

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