1. Artificial Intelligence 1.1 A Comprehensive Introduction to AI 1.2 Chemical Space and AI 1.3 Impact of AI in Computational Chemistry 1.4 Machine Learning Applications in Computational Chemistry 1.4.1 ML and QSAR 1.
4.2 Property Prediction 1.4.3 Generative Models for Molecular Design 1.4.4 Chemical Reactions 1.5 AI-Driven Approaches in Quantum Chemistry 1.5.
1 Quantum Property Prediction 1.5.2 Quantum Circuit Optimization 1.5.3 Quantum Machine Learning for Molecular Systems 1.6 Future and Challenges of AI in Chemistry Conclusion References 2. Machine Learning 2.1 Fundamental Concepts of Machine Learning 2.
2 Understanding the Foundations 2.2.1 Human Learning and Types of Human Learning 2.2.2 Human versus Machine Learning 2.2.3 Supervised and Unsupervised Learning 2.2.
4 Neural Networks and Deep Learning 2.2.5 Generative Learning 2.3 Essential Steps in Applying Machine Learning 2.3.1 Preparation and Handling of Data 2.3.2 Feature Engineering and Feature Selection 2.
3.3 Model Building and Validation 2.3.4 Evaluation 2.3.5 Applicability Domain and Deployment 2.4 Machine Learning in Various Fields of Natural Sciences 2.4.
1 Material Sciences 2.4.2 Chemical Sciences 2.4.3 Life Sciences 2.4.4 Environmental Sciences 2.4.
5 Agricultural Sciences 2.5 From Machine Learning to Deep Learning 2.6 Rise of Generative Models and Industry 5.0 Conclusion References 3. Scientific Computing Using Python 3.1 Python Basics 3.1.1 Creating Python Environment 3.
1.2 Installation of Packages and Libraries 3.1.3 Python Workbenches 3.2 Handling Numeric Data with NumPy 3.2.1 Basic Computing Operations 3.2.
2 Arrays and Indexing 3.2.3 Vectorization 3.2.4 Functions and Methods 3.2.5 Dealing with Missing Data 3.2.
6 Generating Random Numbers 3.3 Utilities of Pandas 3.3.1 Reading and Handling Data with Pandas 3.3.2 Selecting and Indexing 3.3.3 Advanced Indexing 3.
3.4 Handling Text Data 3.3.5 Statistical Functions with Pandas 3.4 Visualization with Matplotlib and Seaborn 3.4.1 Plotting Basics 3.4.
2 Various Types of Plots in Matplotlib 3.4.3 Overlaying and Multifigure Plots 3.4.4 3-Dimensional Plotting 3.4.5 Seaborn Plot Types 3.4.
6 Categorical Plots 3.4.7 Distribution and Pair Plots 3.4.8 Correlation and Heatmaps 3.5 RdKit for Chemoinformatics 3.5.1 Molecule Representations 3.
5.2 Visualizing Structures 3.5.3 Basic Operations with RdKit 3.5.4 Finding Descriptors 3.5.5 Atoms and Bonds 3.
5.6 Similarity and Searching Patterns 3.6 Chemypy Package 3.6.1 Basics and Installation 3.6.2 Handling Reactions 3.6.
3 Chemical Kinetics 3.6.4 Finding Properties 3.6.5 Other Utilities Conclusion References 4. Machine Learning with Python 4.1 Scikit-learn Library in Python 4.2 Data Representation and Generation 4.
2.1 Generating Synthetic Data 4.2.2 Exploring Data Sets 4.2.3 Data Preprocessing and Preparation 4.2.4 Feature Engineering 4.
3 Supervised Machine Learning 4.3.1 Training for Linear Regression 4.3.2 Multi-Linear Regression 4.3.3 Classification with Logistic Regression 4.3.
4 Random Forest-Based Classification 4.4 Unsupervised Machine Learning 4.4.1 Dimensionality Reduction 4.4.2 Clustering with Partitioned Algorithms 4.4.3 Hierarchical Clustering 4.
5 Evaluation Metrics 4.5.1 Confusion Matrix 4.5.2 Accuracy and Error 4.5.3 Precision and Recall 4.5.
4 ROC-AUC 4.5.5 Cluster Analysis Metrics 4.6 Case Studies Conclusion References 5. Evolution of Computational Chemistry 5.1 Overview of Computational Chemistry 5.1.1 Computational Tools and Techniques 5.
1.2 Significance and Contributions 5.2 Era of High-Performance Computing 5.2.1 Role of Supercomputing in Computational Chemistry 5.2.2 Parallelization and Acceleration Techniques 5.2.
3 Cloud Computing and Distributed Computing 5.3 Software and Tools 5.3.1 Overview of Computational Chemistry Software 5.3.2 Open-Source vs. Commercial Software 5.3.
3 Popular Software Packages and Their Capabilities 5.3.4 Intersection of Machine Learning and Computational Chemistry 5.3.5 Predictive Modelling and Property Estimation 5.4 Recent Advances and Future Directions 5.4.1 Quantum Computing and Its Impact 5.
4.2 Multiscale Modelling and Simulation 5.4.3 Emerging Fields and Interdisciplinary Applications Conclusion References 6. Structure-Property Relationships 6.1 Fundamentals of Structure-Property Relationships 6.1.1 Defining Structure and Property 6.
1.2 Understanding Structure-Property Relationships 6.1.3 Interlinking Molecular/Structural Features and Properties 6.2 Chemical Structure and Property Correlations 6.2.1 Molecular Structure and Properties 6.2.
2 Electronic Structure and Optical Properties 6.2.3 Topological and Geometrical Descriptors 6.3 Quantitative Structure-Property Relationships (QSPR) 6.3.1 Developing QSPR Models 6.3.2 Regression Analysis and Parameterization 6.
3.3 Applicability and Limitations 6.4 Quantitative Structure-Activity Relationships (QSAR) 6.4.1 QSAR in Drug Design 6.4.2 Molecular Descriptors in QSAR 6.4.
3 Predictive Modelling and Toxicology 6.5 Materials Science and Structure-Property Relationships 6.5.1 Atomic and Crystal Structures 6.5.2 Mechanical Properties of Materials 6.5.3 Thermodynamic and Electronic Properties 6.
6 Biological Systems and Structure-Property Relationships 6.6.1 Proteins and Enzymes 6.6.2 DNA and RNA 6.6.3 Structure-Function Relationships in Biology Conclusion References 7. Reaction Modelling 7.
1 Overview of Reaction Modelling 7.2 Chemical Kinetics 7.2.1 Basics of Chemical Reactions 7.2.2 Reaction Rate and Rate Laws 7.2.3 Factors Affecting Reaction Rates 7.
3 Reaction Mechanisms 7.3.1 Elementary Reactions vs. Overall Reactions 7.3.2 Reaction Intermediates 7.3.3 Reaction Mechanism Determination 7.
3.4 Analysis of Reaction Potential Energy Surface 7.4 Reaction Rate Constants 7.4.1 Arrhenius Equation 7.4.2 Temperature Dependence 7.4.
3 Catalysis and Reaction Rate Constants 7.5 Reaction Modelling Approaches 7.5.1 Homogeneous vs. Heterogeneous Reactions 7.5.2 Batch, Plug Flow, and Continuous Stirred Tank Reactors 7.5.
3 Ideal vs. Non-Ideal Reactors 7.6 Numerical Methods for Reaction Modelling 7.6.1 Finite Difference Methods 7.6.2 Finite Element Methods 7.6.
3 Computational Fluid Dynamics (CFD) Conclusion References 8. Computer-Aided Drug Design 8.1 Introduction to Computer-Aided Materials (Drug) Design 8.2 Drug Discovery Process 8.2.1 Target Identification and Validation 8.2.2 High-Throughput Screening (HTS) 8.
2.3 Hit-to-Lead Optimization 8.2.4 Lead Optimization and Preclinical Testing 8.3 Molecular Modeling in Drug Design 8.3.1 Protein Structure Prediction 8.3.
2 Ligand Docking and Binding Affinity Prediction 8.3.3 Pharmacophore Modeling 8.3.4 Quantitative Structure-Activity Relationship (QSAR) Studies 8.4 Virtual Screening and Compound Selection 8.4.1 Structure-Based Virtual Screening 8.
4.2 Ligand-Based Virtual Screening 8.4.3 Fragment-Based Drug Design 8.5 De Novo Drug Discovery 8.5.1 De Novo Molecular Design 8.5.
2 Computer-Generated Molecule Libraries 8.5.3 Optimization Algorithms in Rational Drug Design 8.6 Chemoinformatics and Bioinformatics 8.6.1 Molecular Databases and Data Mining 8.6.2 Sequence Analysis in Drug Discovery 8.
6.3 Chemoinformatics for Compound Analysis 8.7 ADME/Toxicity Prediction 8.7.1 Absorption, Distribution, Metabolism, and Excretion (ADME) 8.7.2 Predicting Drug Toxicity 8.7.
3 Risk Assessment in Drug Design Conclusion References 9. Materials Modelling 9.1 Introduction 9.1.1 Role of Materials Modelling in Science and Engineering 9.1.2 Overview of Computational Methods 9.2 Materials 9.
2.1 Predicting Mechanical Properties 9.2.1.1 Strength and Elasticity 9.2.1.2 Ductility and Toughness 9.
3 Material Design for Specific Applications 9.3.1 Aerospace Materials 9.3.2 Automotive Materials 9.3.3 Building and Construction Materials 9.4 Electronic and Photonic Materials 9.
4.1 Semiconductor Device Simulation 9.4.1.1 Transistor Design 9.4.1.2 Optoelectronic Device Modelling 9.
5 Superconductors and Magnetic Materials 9.5.1 High-Temperature Superconductors 9.5.2 Magnetic Data Storage Materials 9.6 Energy Materials 9.6.1 Fuel Cell and Battery Materials 9.
6.1.1 Lithium-Ion Batteries 9.6.1.2 Fuel Cell Catalysts 9.6.2 Solar Cell Materials 9.
6.2.1 Photovoltaic Device Optimization 9.6.2.2 Organic Solar Cells 9.7 Nanomaterials and Nanotechnology 9.7.
1 Modeling at the Nanoscale 9.7.2 Nanoparticle Synthesis and Properties 9.7.3 Nanocomposite Materials Conclusion References 10. Electronic Structure Calculation, Ab Initio, DFT, and MD Simulation 10.1 Introduction to Quantum Mechanics 10.1.
1 Wave Functions, P.