The course will involve a little bit of python (I will assume no prior knowledge). A beginners course on python, courtesy of my Surrey colleague James Adams, is here. That webpage has its own set of notes and examples, and has links to a number of standard introductory references.
Lecture notes:
- Crystals & Crystallisation pdf notes
- What are crystals?
- Disordered crystals & XRD
- Nucleation of crystals
- Crystal growth
- Transport: diffusion & flow pdf notes (In Jan 2019 I taught at another EU school, so I updated and expanded my transport notes, new version here these are a bit better than the May 2018 Maynooth version, I think).
- Mixing needs flow & diffusion
- Crystal growth: The role of transport
- Phoresis: diffusion of one thing in response to a gradient in another
- Data analysis pdf notes
- We all need models … to make predictions
- Linear regression & two types of errors in fitting
- The problem of sampling high dimensional space – finding a needle in multidimensional haystack
- Partial solutions to searching high dimensional space: 1) get more data, 2) analyse data better, including scoring outcomes & image analysis
Note to RAMP ESRs: it was a pleasure to run this little course, if you have any comments, spot an error in the notes, just drop me an email, Richard
Python programs:
Python programs are just text files, and have suffix .py. They can be edited by any text editor, or by something like Spyder.
Fitting etc of data:
python program for linear regression
python program to demonstrate fitting errors for 1) noisy data, and 2) model wrong
Simple model of diffusion:
python program to demo diffusion starting from the origin, and plotting in python
Analysing a trial outcome:
How to rationally use info from one trial (eg that trial with a particular concentration of a precipitant resulted in formation of a precipitate), to come up with the best guess for the next precipitant concentration to try:
1.0 3.1
2.0 5.3
3.0 6.9
4.0 9.1
5.0 10.9
6.0 12.3
7.0 15.1
Website of Richard Sear 