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

• 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
• Physical Description: 1 online resource (XVIII, 522 p.)

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 take chemical research and engineering to the next level. It covers modern aspects like Big Data, Artificial Intelligence and Quantum computing.

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