Advanced Topics in Chemical Physics: Introduction to Machine Learning

This repository contains three lectures and three workshop sessions on introducing machine learning concepts in the advanced physical chemistry module at UoE.

Author

Dr Antonia Mey -- [email protected].
Jasmin Güven

Workshop Notebooks

Units	Materials
Unit_01: Clustering and Dimensionality Reduction Reduction
First noteboook: Clustering
Second notebook: Dimensionality reduction
Third notebook: Application
Unit_02: Regression and Classification	cancelled due to strike
Unit_03: Deep Learning for Solubility Classification
First noteboook: Intro to Pytorch
Second notebook: Solubility classification

Local installation

1. Install anaconda.

2. Create a new environment:

   conda create -n ml_chem

3. Activate the environment:

   conda activate ml_chem

4. Install mamba to make the installation of packages faster.

   conda install -c conda-forge mamba

5. Install all the required packages with mamba:

   mamba install -c conda-forge scikit-learn matplotlib pandas

For Unit_03 you will also need to install

mamba install -c conda-forge rdkit seaborn

and

mamba install pytorch torchvision torchinfo -c pytorch

Project

Release: week 4 Report Deadline: TBC
Weight: 20%

Summary of Lectures

Lecture 1:

• What is machine learning?
• Examples of machine learning (in Chemistry)
• Introduction to unsupervised learning:
  • Clustering (k-means and others)
  • How does actual input data look like?
• Molecular fingerprints and nomenclature
• Unsupervised learning continued:
  • Dimensionality reduction (PCA)
  • Dimensionality reduction (tICA)
  • t-SNE

Lecture 2:

• Optimization
• Regressions
• Classification
• Classifications in practice:
  • Random Forests
  • Support vector machine

Lecture 3:

• Shallow Learning
• Deep Learning
  • Multilayer perceptron
  • GCN, Transformers


Learning Outcomes

• Understand the main pillars of machine learning
• Know about different clustering techniques as part of unsupervised learning
• Be able to use common nomenclature used in machine learning
• Use Principle component analysis to reduce the dimensions of a data set
• Understand how a regression problem can be cast as a machine learning problem
• Be aware of how random forests and multilayer perceptrons can be used in a classification problem

Additional Resources

A handout with additional resources can be found here.