Reddit reviews Understanding Molecular Simulation: From Algorithms to Applications (Computational Science Series, Vol 1)

Computational physicist here, one of the biggest problems I dealt with was not actually understanding the science behind what I'm doing. You can be as clever a programmer as you want, but you're still a physicist first. Don't code things that you don't understand. I hugely suggest this book when you're getting started out.

Python's super useful for scripting, generating a lot of files, and general busywork, and SciPy/NumPy are pretty awesome for data analysis. MatPlotLib is probably the greatest library in existence, because it makes publication quality graph generation absolutely trivial. Chances are, you'll be forced to learn it in grad school anyways, so you might as well start now.

Re:scalability, I highly suggest learning CUDA and Open MPI. MPI stands for Message Passing Interface, and is used to parallelize code between nodes on most university clusters. It will save you a lot of time and energy, and isn't that different from C. CUDA, or Compute Unified Device Architecture, is NVidia's architecture for GPUs, which you probably have on your laptop. GPUs are usually a lot better at (necessarily parallel) simulations then CPUs, so they're pretty popular with a lot of major labs and computationally intensive tasks like financial analysis.

For readability, comment everything. Not quite every line, but certainly every class and function. If someone who's never seen it before doesn't know what it does, you're doing it wrong. Always err on the side of too many comments. On that note, if you don't already, I strongly recommend Doxygen, which automatically generates documentation in pretty html format from your comments.

EDIT: Whoops forgot acronyms

A great book about this subject is Understanding Molecular Modeling, by Daan Frenkel

http://www.amazon.com/Understanding-Molecular-Simulation-Second-Edition/dp/0122673514

Basically in order to understand molecular modeling you need to have a good understanding of thermodynamics (more statistical mechanics, but also classical thermodynamics).

For a good free resource on graduate level thermodynamics and molecular modeling I recommend Scott Schell's lecture notes (He is a professor at UC Santa Barbara)
Molecular Modeling Class Notes:
http://www.engr.ucsb.edu/~shell/che210d/assignments.html

Thermo and Stat Mech Class Notes:

http://www.engr.ucsb.edu/~shell/che210a/


edit to add more info:

Also- if you are doing free energy methods check out
alchemistry.org

Keep in mind that molecular modeling is a huge field with tons of methods, tons of open source and closed source software packages. More info about what specifically you want to do and what software you want to use would be helpful.
I use GROMACS to do molecular dynamics simulations, so if you have questions specifically about that I should be able to give some assistance.

Personally I make a distinction between scripting and programming that doesn't really exist but highlights the differences I guess. I consider myself to be scripting if I am connecting programs together by manipulating input and output data. There is lots of regular expression pain and trial-and-error involved in this and I have hated it since my first day of research when I had to write a perl script to extract the energies from thousands of gaussian runs. I appreciate it, but I despise it in equal measure. Programming I love, and I consider this to be implementing a solution to a physical problem in a stricter language and trying to optimise the solution. I've done a lot of this in fortran and java (I much prefer java after a steep learning curve from procedural to OOP). I love the initial math and understanding, the planning, the implementing and seeing the results. Debugging is as much of a pain as scripting, but I've found the more code I write the less stupid mistakes I make and I know what to look for given certain error messages. If I could just do scientific programming I would, but sadly that's not realistic. When you get to do it it's great though.

The maths for comp chem is very similar to the maths used by all the physical sciences and engineering. My go to reference is Arfken but there are others out there. The table of contents at least will give you a good idea of appropriate topics. Your university library will definitely have a selection of lower-level books with more detail that you can build from. I find for learning maths it's best to get every book available and decide which one suits you best. It can be very personal and when you find a book by someone who thinks about the concepts similarly to you it is so much easier.
For learning programming, there are usually tutorials online that will suffice. I have used O'Reilly books with good results. I'd recommend that you follow the tutorials as if you need all of the functionality, even when you know you won't. Otherwise you get holes in your knowledge that can be hard to close later on. It is good supplementary exercise to find a method in a comp chem book, then try to implement it (using google when you get stuck). My favourite algorithms book is Numerical Recipes - there are older fortran versions out there too. It contains a huge amount of detailed practical information and is geared directly at computational science. It has good explanations of math concepts too.

For the actual chemistry, I learned a lot from Jensen's book and Leach's book. I have heard good things about this one too, but I think it's more advanced. For Quantum, there is always Szabo & Ostlund which has code you can refer to, as well as Levine. I am slightly divorced from the QM side of things so I don't have many other recommendations in that area. For statistical mechanics it starts and ends with McQuarrie for me. I have not had to understand much of it in my career so far though. I can also recommend the Oxford Primers series. They're cheap and make solid introductions/refreshers. I saw in another comment you are interested potentially in enzymology. If so, you could try Warshel's book which has more code and implementation exercises but is as difficult as the man himself.

Jensen comes closest to a detailed, general introduction from the books I've spent time with. Maybe focus on that first. I could go on for pages and pages about how I'd approach learning if I was back at undergrad so feel free to ask if you have any more questions.



Out of curiosity, is it DLPOLY that's irritating you so much?