Skip to content

Timetable

Timetable: subject to change. Proposed dates: 5-9 April 2027.

Day Time Activity Speaker(s)
Day 1 9:00-9:50 Registration
9:50-10:00 Welcome
10:00-11:00 Lecture 1
11:00-11:30 Welcome
11:30-12:30 Lecture
12:30-13:30 Lunch
13:30-15:00 Introduction to Machine Learning Lukas Hörmann
15:00-15:30 Coffee Break
15:30-17:00 Practical Session Lukas Hörmann
17:30-20:00 Poster Session
Day 2 08:30-09:30 Arrival, Coffee and Pastries
09:30-11:00 Descriptors and Unsupervised Learning Nong Artrith
11:00-11:30 Coffee Break
11:30-12:30 Practical Session Nong Artrith
12:30-13:30 Lunch
13:30-15:00 Bayesian Optimisation Laura Helleckes
15:00-15:30 Coffee Break
15:30-17:00 Practical Session Laura Helleckes
17:30-18:30 Research Seminar 1 Irea Mosquera-Lois (CuspAI)
Day 3 08:30-09:30 Arrival, Coffee and Pastries
09:30-11:00 Neural Networks and Convolutional Neural Networks Keith Butler
11:00-11:30 Coffee Break
11:30-12:30 Practical Session Keith Butler
12:30-13:30 Lunch
13:30-15:00 Graph Neural Networks Alex Ganose
15:00-15:30 Coffee Break
15:30-17:00 Practical Session Alex Ganose
17:30-20:00 BBQ and Networking
Day 4 08:30-09:30 Arrival, Coffee and Pastries
09:30-11:00 Introduction to Machine-Learned Interatomic Potentials for Materials and Molecules Ioan-Bogdan Magdău
11:00-11:30 Coffee Break
11:30-12:30 Practical Session Ioan-Bogdan Magdău & Alin Elena
12:30-13:30 Lunch
13:30-15:00 Advanced MLIPs for Materials and Molecules Ioan-Bogdan Magdău
15:00-15:30 Coffee Break
15:30-17:00 Practical Session Ioan-Bogdan Magdău & Alin Elena
17:30-18:30 Research Seminar 2 Christoph Schran
Day 5 08:30-09:30 Arrival, Coffee and Pastries
09:30-11:00 Diffusion and Flow-Based Models Bradley Martin
11:00-11:30 Coffee Break
11:30-12:30 Practical Session Bradley Martin
12:30-13:30 Lunch
13:30-15:00 Autoregressive Models Sean Kavanagh
15:00 onwards Closing Remarks and Departure

Intro to ML and Descriptors:

Outline and Topics covered:

Lecture

  • ML and its applications in general and specifically in comp. phys research
  • Its role within research/science
  • Basic definitions and terminology
  • Mathematical basics on optimisation, loss functions
  • Automatic differentiation, training, stochastic optimisation
  • Accuracy of Models (testing, validation, quantifying accuracy)
  • Overview and short summary of types/classes of ML models
  • multivariate linear regression (in detail)
  • Gaussian Processes (in detail)
  • Kernel methods (in detail)
  • Reinforcement learning (dynamic programming, Bellman eq.)
  • classification, decision trees

Workshop

  • Data generation, curation, and analysis/visualisation
  • Linear regression, Bayesian Linear Regression, Gaussian Regression
  • Hyperparameter optimisation
  • Uncertainty quantification

Lecture:

  • A typical machine learning workflow
  • Model optimisation (cross validation techniques)
  • Uncertainty of Models (UQ)
  • Data representation and data cleanup. Is my data set any good?
  • Featurisation of molecules and materials
  • mathematical requirements on descriptors and nomenclature
  • global descriptors
  • fragment, fingerprints, cliques, functional group-based descriptors
  • basics of atom-centred descriptors

Workshop:

  • preparing and analysing a dataset of different molecules and a dataset of MD trajectories (differences in exploring composition and configuration space)
  • generating different global, local (atom centred, fingerprints, fragment-based) descriptors and analysing their suitability and expressiveness (basic PCA, discriminators and decision trees)
  • figuring out what descriptors work best for what datasets
  • training, optimising and benchmarking various ML models with the descriptors
  • workshops based on simple rdkit, scikit-learn, ASE, dscribe, JAX functionality

Expected Outcome:

1.Know the role of machine learning in the computational physical sciences 2.Understand the basic terminology of machine learning (“Slang busting”) 3.Have an overview of methodologies and how they connect 4.Understand how to approach a typical machine learning workflow 5.Know how to prepare and analyse datasets 6.Be able to validate and optimise models with cross-validation 7.Know basic approaches to featurisation and representation in chemistry 8. Know how to evaluate and assess prediction errors and uncertainties

Unsupervised ML:

Outline and Topics covered:

  • Exploratory data analysis
  • Curse of dimensionality in chemical problems
  • Dimensionality reduction – principle component analysis, manifold learning
  • Clustering – k-means, k-nearest neighbours, hierarchical clustering

Expected Outcomes:

  • Understand the role of unsupervised learning in typical machine learning workflows
  • Understand and apply linear and non-linear dimensionality reduction on data
  • Evaluate and apply clustering algorithms on data
  • Design and apply unsupervised strategies for chemical data

Graph Neural Networks:

  • Outline and Topics covered:
  • Directed and undirected graphs – concepts and examples
  • Machine learning with graphs
  • Convolutions on images and graphs
  • Principles of graph neural networks
  • Message passing and locality
  • Oversmoothing and oversquashing
  • Message passing graph neural networks for crystals and molecules
  • Equivariance
  • Universal GNNs

Expected Outcomes:

  • Explain the use of graph neural networks in chemistry and materials science
  • Identify the main design choices when building GNNs
  • Compare the capabilities and performance of state-of-the-art GNNs
  • Implement a basic GNN from scratch
  • Train and run a GNN on molecular data

MLIPs:

Outline and Topics covered:

  • Brief Intro to MLIPs: Context and Overview
  • Anatomy of a potential: locality, E0s, forces
  • Atomic Descriptors: Symmetry, Smoothness, Completeness (link to Reinhard and Kim materials)
  • Models Architectures: Linear, Kernell, Message-passing NNs (MACE) (link to Reinhard, Keith and Alex)
  • From RMSE to MD Stability and Accuracy
  • MLIPs in practice: example battery electrolytes
  • Iterative training, committees and active learning
  • Foundational models and fine tuning

Expected Outcomes:

  • Learn how to fit MLIP (MACE)
  • Fixed test sets and landscape exploration (MD stability/accuracy)
  • Learn how to improve the training set using iterative training
  • Committee error estimates and active learning (covered in Intro)
  • Using foundational models and fine tuning on new systems

BO Basics

  • What is BO? And why is it well-suited to chemistry?
  • What are the components of a BO algorithm? (Surrogate model and acquisition function discussion)
  • Crash course on Gaussian Processes as surrogate models
  • Crash course on acquisition functions with an emphasis on most common for chemistry applications

Workshop

  • BO implementation for a simple mathematical function

BO for chemistry

  • Considering what we covered in Lecture 1, what do we need to change or include to make this useful for chemistry?
  • Role of chemical descriptors
  • How do we formulate problems for BO in chemistry?
  • Overview of complex, state-of-the-art BO implementations for chemistry.
  • Overview of chemistry-specific BO tools

Workshop

  • Working in pairs, optimise a Suzuki-Miyaura cross-coupling reaction using the fewest resources.
  • There is a code-based implementation, and a GUI-based implementation of the materials for students who may have less Python experience.

Expected Outcomes

  • Understand the basic principles of BO
  • Know the components that comprise a BO algorithm (surrogate models, acquisition functions, etc.)
  • Have an overview of some more complex BO algorithms (multi-objective, multi-fidelity)
  • Understand the capabilities and limitations of BO in chemistry
  • Know how to start to formulate a chemical problem for BO

Diffusion Models

Outline and Topics covered:

  • Diffusion as a physical process: random walks, Langevin dynamics, and links to statistical mechanics/thermodynamics
  • From physics to ML: score functions, denoising, and the forward/reverse processes
  • Modern diffusion in ML: score matching, denoising diffusion probabilistic models and latent diffusion
  • Crystal generation using diffusion models: introducing MatterGen and Chemeleon and how they work

Expected Outcomes:

  • Build intuition for diffusion models from both physics and ML perspectives
  • Know the the components that underly diffusion models
  • Overview of modern SOTA diffusion model architectures and training procedures
  • Learn how to implement and run a simple diffusion model
  • How to use pretrained diffusion models (MatterGen/Chemeleon) to generate candidate material structures