Course Overview
The Solid Data Summer School is a hands-on, project-based program designed to equip participants with skills in data-driven approaches for the analysis of solid-state materials. The course is structured over four days, combining introductory lectures, team-based research projects, and networking opportunities. There will be some pre-work provided to make sure the participants have the required computational skills to get the most out of the course.
Topics
Participants will learn how to use big data and other computational approaches to improve the analysis of experimental data in solid-state chemistry. Taught sessions will include
- Some fundamental maths behind tools like machine learning,
- Aspects of computation in materials science, including
- interacting with databases,
- simulating materials properties
- data simulation from models that can be compared directly with experiments
Team projects
The projects are designed to reinforce the lectures through hands-on work with real (and messy!) materials data. You will get to see how to use tools to help with structural characterisation and work with data with a critical and analytical perspective. We will cover areas such as
- Using data-driven methods to tackle research problems
- Understanding model limitations
- Quantifying confidence in your results We will use datasets involving real world materials research problems. Teams can devise their own approach so you can choose to explore side questions that emerge from the data that are interesting or relevant to your field.
Draft Timetable
| Day 1 | Day 2 | Day 3 | Day 4 | |
|---|---|---|---|---|
| Morning | Stats and programming | Research team project work | Clustering structural information | Dragon’s Den |
| Afternoon | Databases, data, and the group project | Databases and the CSD | Keith Butler seminar | — |
| Evening | Social and poster session | Pizza and hacking | School dinner | — |
Accessibility & Resources:
All materials are provided in advance and made openly available post-school to ensure inclusivity and continued learning.
- Please find the “textbook” for the Stats and Programming section online.
- The stats and programing worksheet can be found here with worked answers.
- Notebooks and associated Python files for the diffraction and crystals section.
- Background on accessing databases via and API is available in the following GitHub repository.
- Datasets for research team project:
Outcomes:
Participants will gain experience in data analysis, computational techniques, and collaborative research, with a focus on drawing meaningful scientific conclusions from experimental data.