I would like to do the simulation of carbohydrates ( cellulose) in Lammps. I used a cellulose builder toolkit ( Thiago C. F. Gomes, Munir S. Skaf, Journal of Computational Chemistry 2012, Volume 33, Issue 14, pages 1338-1346. DOI: 10.1002/jcc.22959 (wiley.com)) for generating the cellulose bundles.
what kind of information does this toolkit generate? or more specifically what file format does it write out? does it contain all the necessary information or only the positions and elements?
After that, I used VMD as well as ovito to create the data file which is required to do the simulation in Lammps. However, in data files, the charges are missing. So, could you please tell me, how should I write the data file correctly?
visualization tools can only write out what you feed them. so if your original data does not contain charge information, the visualization tools will not add it. they are for the most part agnostic to the chemical nature of the system you are looking at anyway, but only consider it a pile of numbers and strings.
On the other hand, I used atom style_full, in the manual it was mentioned that I need to use it for the simulation of biomolecules. Am I right?
what atom style you need to use depends on the force field that you want to use. atom style full is a superset of multiple more specific atom styles so that it can be used for anything whether it has charges or not and bond topology data or not. there are likely more steps needed to get a meaningful simulation.
i suggest to start with something simpler, e.g. what is explained here:
to get your feet wet with how to set up simulations for molecular force fields. then graduate to do systems with sugar molecules and then finally your actual target.
Or, Do I need to use AMBER files for creating the data files in Lammps.
Actually, I am a new user of Lammps. This is my first post.
…and it feels you are rushing into the topic of your research without having acquired the necessary experience. doing MD simulations is as much a craft as it is a science and thus it is often required to do some kind of apprenticeship where you learn about the basic processes and workflows (including analysing results and comparing to existing data) before you start with something new.
you have to keep in mind that this is a very mature methodology, so all the “easy stuff™” has long been done and every project worth doing research on will be somewhat complex and require some effort to get all the basics right. there are many (simple) mistakes that you can make that are not immediately obvious, so if you rush into your actual project too quickly you may find out later that you wasted time and effort because of silly simple mistakes that you were not aware of at the time.