Modeling lab

Welcome to "Modeling lab"

• General Announcements (Forum)
• Course Introduction (File)
• Group Choice CH-315 2025 (Group choice)
• Introduction to adsorption in MOFs (File)
• Lecture notes Machine Learning (File)

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Computational Carpentry

• Introduction to Python (Page)
• Module project: Contributions (Questionnaire)

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Modeling Adsorption of Gases in Porous Materials

• COURSE PLAN: Modeling Adsorption of Gases in Porous Materials (Page)
• Block 2, Assignment 1: Contributions (Questionnaire)
• Block 2, Assignment 2: Contributions (Questionnaire)
• Block 2, Project: Contributions (Questionnaire)
• Part 1 (Text and media area)
• 1.1 Classical Thermodynamics: Equilibrium (URL)
• 1.2 Classical Thermodynamics: Langmuir Isotherms (URL)
• 1.3 Classical Thermodynamics: Henry Coefficients (URL)
• 1.4 Classical Thermodynamics: Temperature dependence of the Henry Coefficients (URL)
• 1.5 Classical Thermodynamics: Heats of Adsorption (URL)
• Part 2The slides for this part (all the videos) ca... (Text and media area)
• 2.1 Statistical Thermodynamics and Molecular Simulation: introduction (URL)
• 2.2 Statistical Thermodynamics and Molecular Simul... (Text and media area)
• 2.2.1 Statistical Thermodynamics and Molecular Simulation: Basic Assumption (URL)
• 2.2.2 Statistical Thermodynamics and Molecular Simulation: equilibrium (URL)
• 2.3 Statistical Thermodynamics and Molecular ... (Text and media area)
• 2.3.1 Statistical Thermodynamics and Molecular Simulation: canonical ensemble (URL)
• 2.3.2 Statistical Thermodynamics and Molecular Simulation: NVT simulations (URL)
• 2.3.3 Statistical Thermodynamics and Molecular Simulation: grand-canonical ensemble (URL)
• 2.3.4 Statistical Thermodynamics and Molecular Simulation: μVT simulations (URL)
• 2.4 Statistical Thermodynamics and Molecular Simulation: (bonus) chemical potential from a simulation (URL)
• 2.5 Statistical Thermodynamics and Molecular Simul... (Text and media area)
• 2.5.1 Statistical Thermodynamics and Molecular Simulation: CO2 in MOF-74 (URL)
• 2.5.2 Statistical Thermodynamics and Molecular Simulation: Xe/Kr separations (URL)
• Part 3Molecular Simulation of Adsorption. (Text and media area)
• 3.1 Introduction to AiiDA and AiiDA lab (URL)
• 3.2 Overview of AiiDA lab interface (URL)
• 3.3 Overview of the AiiDA lab LSMO application (URL)

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Machine Learning

• Assignment (Text and media area)
• Module Assignment: Contributions (Questionnaire)
• Project (Text and media area)
• Project description and explanation of Supervised ML for Gas Adsorption in MOFs (Page)
• Module Project: Contributions (Questionnaire)
• A simple example of machine learning in chemistry (Page)
• Part 0. Introduction to the module  (Text and media area)
• 2.0. Introduction to the machine learning module (URL)
• 2.0 slides (File)
• 2.1. Types of machine learning (URL)
• 2.1 slides (File)
• Part 1. Supervised machine learning (Text and media area)
• 2.2. supervised machine learning workflow (URL)
• 2.2. slides (File)
• 2.3. featurisation (URL)
• 2.3 slides (File)
• 2.4. splitting data (URL)
• 2.4 slides (File)
• 2.5. regression (URL)
• 2.5 slides (File)
• 2.6. bias-variance trade-off (URL)
• 2.6 slides (File)
• 2.7. hyperparameters (URL)
• 2.7 slides (File)
• 2.8. kernel models (URL)
• 2.8 slides (File)
• 2.9. model interpretation (URL)
• 2.9 slides (File)
• 2.10. summary of supervised learning (URL)
• 2.11. Feature selection (Page)
• 2.12. Feature projection (Page)
• 2.13. Feature learning (Page)
• Part 2. Examples (Text and media area)
• 2.14. Applications of Supervised Machine Learning (Page)

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Data in Chemistry and Electronic Lab Notebooks (Optional extra materials)

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