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**- 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