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

Description

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. 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