A little bit of coding can a long way in matter modeling. Coding is not used only to write big programs but can be used for scripting, data processing, automation, and more. But sometimes many resources (books, papers, etc.) are generic and superficial.
Are any good resources to learn to code in the context of matter modeling? What do you recommend?
For a beginner in the field, most often the difficulty would be in working with unix-like environments, which is the only way to access many of the matter modeling software and probably the most convenient way to analyse the outputs of the calculations. And this is often learned the hard way by searching the internet on how to work with different tools for different purposes, which often takes a long time to get going with and is most often frustrating for people less familiar with the working of these systems. This course introduces one to the basic components of a unix-like environment, working with shell, scripting, editing files, data analysis and many more which would help build a base understanding of these systems from which one can easily learn the specific tools required for one's work. It's named aptly as "the missing semester" as it is often not covered while teaching computational techniques and is left to the learner to figure out on their own.
Code can be used to automate, derive insights and to calculate more observations with the given set of data.
Automation: This can be done via bash scripts or through the os module in python. This is an example bash script
Deriving insights and data manipulation: This can be done using small python scripts and some modules that may come in handy are:
Hope this helps :)
I recommend Computational Studies of Quantum Spin Systems by Anders Sandvik arXiv:1101.3281, or AIP Conf. Pro. 1297, 135 (2010).
This review is designed to teach you how to write quantum Monte Carlo simulations of spin systems like the Heisenberg model. There are also example codes from the workshop on Sandvik's website (note of caution: there may be a bug where the random number generator doesn't work for the SSE code, so you may have to substitute your own RNG).
This question is quite generic but I always plug the online DFT book by Kitchin. Its a good resource showing how python can run VASP which is probably a good starting point.
Allen & Tildesley. One of two molecular mechanics bibles. They have a github accounts with a ton of python and fortran codes
Frenkel & Smit, and they have fortran 90 code that you can find. A solid reference book.
D. C. Rapaport The Art of Molecular Simulation. Plenty of codes, written in C.
Andrew Leach Molecular Modelling: Principles and Applications.
The first two cover molecular dynamics and Monte Carlo quite well. These are mandatory reference books. The third covers molecular dynamics quite well.
Here is an online course: http://compmatphys.epotentia.com/
Description:
It is possible to predict properties of materials “from scratch” or “ab initio”: by applying the laws of quantum physics to the atoms that make up the material. The methods for doing this have been developed by solid state physicists, and are now sufficiently mature to tackle materials engineering problems. These so-called “ab initio methods” are becoming a mandatory item in the toolbox of modern materials scientists. This course teaches you in a practical and hands-on way how to become a responsible user of these tools.
I strongly advise you to take the previous course as the starting point. After you complete that, you can find more tutorials and workshops from the website of Quantum Espresso.
