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Simulation of molecular dynamics in Python

By : user3850978
Date : October 18 2020, 11:12 AM
will be helpful for those in need Have you considered SimPy? SimPy is a rather generic Discrete Event Simulation package, but could feasibly meet your needs.
Better yet the Molecular Modelling ToolKit (MMTK) seems more specialized...
code :

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Best language for Molecular Dynamics Simulator, to be run in production. (Python+Numpy?)

By : amcphall
Date : March 29 2020, 07:55 AM
I wish this helpful for you The high performing MD implementations tend to be decidedly imperative (as opposed to functional) with big arrays of data trumping object-oriented design. I've worked with LAMMPS, and while it has its warts, it does get the job done. A perhaps more appealing option is HOOMD, which has been optimized from the beginning for Nvidia GPUs with CUDA. HOOMD doesn't have all the features of LAMMPS, but the interface seems a bit nicer (it's scriptable from Python) and it's very high performance.
I've actually implemented my own MD code a couple times (Java and Scala) using a high level object oriented design, and have found disappointing performance compared to the popular MD implementations that are heavily tuned and use C++/CUDA. These days, it seems few scientists write their own MD implementations, but it is useful to be able to modify existing ones.

Runaway Velocities when implementing Velocity Verlet Algorithm in Periodic Molecular Dynamics Simulation

By : Jack
Date : March 29 2020, 07:55 AM
wish of those help Maybe you figured it out already, but I think the mistake is in the checkBoundaries code for the periodic boundaries. I might be wrong, but I don't think you can use the % operator on floats/doubles. I think the code should be (see also https://en.wikipedia.org/wiki/Periodic_boundary_conditions )
code :
Vector3d op = a.getPosition(); //old position
double w = getWidth();
position.setX( op.getX() - w*((int)(2*op.getX()-1)) );
position.setX( op.getY() - w*((int)(2*op.getY()-1)) );
position.setX( op.getZ() - w*((int)(2*op.getZ()-1)) );

I need help for N-body molecular simulation performance!!(java applet)

By : Carlos Romero
Date : March 29 2020, 07:55 AM
This might help you Instead of using a lookup tables, try using Chebyshev polynomials. Remember that you can exponentiate x^k in only ln(k) steps.
It may seem like a lot of operations, but the fact that in can be done without hitting memory (and therefore not affecting the cache) can make it significantly faster than a lookup table.

OpenMP and OOP (Molecular Dynamics Simulation)

By : Heatwave
Date : March 29 2020, 07:55 AM
To fix this issue Without further details on your simulation type I can only speculate, so here are my speculations.
Did you look into the issue of load balancing? I guess the loop distributes the particles among threads but if you have some kind of a restricted range potential, then the computational time might differ from particle to particle in the different regions of the simulation volume, depending on the spatial density. This is a very common problem in molecular dynamics and one that is very hard to solve properly in distributed memory (MPI in most cases) codes. Fortunately with OpenMP you get direct access to all particles at each computing element and so the load balancing is much easier to achieve. It is not only easier, but it is also built-in, so to speak - simply change the scheduling of the for directive with the schedule(dynamic,chunk) clause, where chunk is a small number whose optimal value might vary from simulation to simulation. You might make chunk part of the input data to the program or you might instead write schedule(runtime) and then play with different scheduling classes by setting the OMP_SCHEDULE environment variable to values like "static", "dynamic,1", "dynamic,10", "guided", etc.

Questions about Molecular dynamics code in "Understanding Molecular Simulation: From Algorithms to Applications&quo

By : J.K.
Date : March 29 2020, 07:55 AM
this will help The example code in "Understanding Molecular Simulation: From Algorithms to Applications", as in many other textbooks, is pseudocode. It is not complete (lack of declarations, for instance) and meant to illustrate the algorithms a bit more explicitly than "just the mathematics". In this book, it is confusing because they write incomplete Fortran for the pseudocode.
This is still a good "general problem": understand how old-fashioned textbook material with Fortran can be used :-)
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