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

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
Main Author: Gressling, Thorsten (Author, http://id.loc.gov/vocabulary/relators/aut)
Format: eBook
Language:English
Published: Berlin ; Boston De Gruyter, [2021]
Series:De Gruyter graduate.
Subjects:
Chemistry > Data processing.
Künstliche Intelligenz.
Massendaten.
Theoretische Chemie.
EBSCO eBooks
Online Access:https://www.lib.tsu.ru/mminfo/2023/EBSCO/2668415.pdf
Table of 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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