Tag Info

CPMD: Car-Parrinello Molecular Dynamics An approximation of BOMD (Born-Oppenheimer MD) where fictitious dynamics is used on the electrons to keep them close to their ground state, so that we do not have to keep solving for their ground state at every single step. We start with Newton's 2nd law (as does classical MD), but instead of the force being calculated ...

There is no way to do this directly in VASP, but you can achieve this goal using the Atomic Simulation Environment (ASE). With your OUTCAR available, do the following: from ase.io import read, write images = read('OUTCAR',index=':') # Read in every iteration interval = 5 # Save .png every N steps for i, image in enumerate(images): if i+1 % interval == 0:...

2nd Generation CPMD Car-Parrinello MD avoids repeatedly solving the electronic problem by propagating the orbitals as if they were particles governed by Newton's equations. This is much more efficient than having to solve at each time step as is done in Born-Oppenheimer MD, though at the cost of decreasing the maximum timestep for the dynamics (too large a ...

I explained Car-Parrinello MD here, and a key point is that it approximates BOMD (Born-Oppenheimer Molecular Dynamics) where the electronic state is minimized (i.e. the ground electronic state is found and used) at each step. The reason for the minimization in CPMD at the first step, is the same as the reason in the earlier BOMD method that it approximates. ...

In can be done easily using python or bash script. You have to keep looking for changes in OSZICAR. Whenever OSZICAR prints 'F' i.e. end of electronic self consistent loop, copy your CONTCAR to other file. There is simple tool in linux system inotify-hookable which will watch over change in CONTCAR file. inotify-hookable -f CONTCAR -c "cp CONTCAR ...

ab initio Ehrenfest Dynamics From Li et.al.,2005, JCP "The Born Oppenheimer (BO) and extended Lagrangian (EL) trajectories are founded on the assumption that a single electronic potential surface governs the dynamics. .. A major limitation of adiabatic trajectories is that they are not applicable to reactions involving nonadiabatic electronic processes, ...

Yes your understanding is correct. It helps to think about BOMD as classical dynamics. The electrons are the springs that hold the masses together and the nuclei are the masses.The two key terms are the internal energy (U) and the kinetic energy of the masses (K). We seek to calculate the positions of the system as a function of time, which we can do ...

Vibrational maps and density of states In water AIMD community, to avoid excessively heavy calculations of molecular moments, researchers use velocity autocorrelation function (which converge much faster), obtaining vibrational densities of state and use vibrational maps to scale the VDOS spectra appropriately. Many researchers also simply calculate ...

I'm not an expert in MD/AIMD software so I can really only discuss methods rather than an actual workflow/software that can carry it out. One approach that has been explored for accelerating AIMD and electronic structure methods in general is to combine calculations on fragments to approximate an electronic structure. John Herbert's Group is doing work in ...

This won't directly answer your question, but a note of caution about focusing too much on optimization. It's definitely worth doing some experimentation to find an efficient combination of memory, # of cpus, etc when you're getting ready to run big simulations. This is especially true for challenging problems where you will be at the limit of your ...

For MD I recommend The Gamma Cantered KPOINTS with Grid mesh 1 1 1.

I think I figured out parts of my question. But would really benefit from a VASP/AIMD expert weighing in. This appears to be the correct way of doing velocity scaling in VASP. And you can scale the velocity every NBLOCK steps (but I'm not sure if/when it's more appropriate to scale every 1 step, 10 steps, etc.) Technically you can I think. I set the ...