Minimizing in the 6 dof of rigid bodies

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SUMMARY

This discussion focuses on minimizing interaction energy between two rigid bodies, specifically molecules, by addressing their six degrees of freedom. The user successfully calculates translational minimization using energy derivatives but seeks guidance on rotational minimization techniques. The conversation explores the use of rotation matrices and quaternions, highlighting the challenges of gimbal lock and constrained minimization. Recommendations include investigating the curl of vectors and considering Euler angles for effective rotation handling.

PREREQUISITES
  • Understanding of rigid body dynamics
  • Familiarity with molecular forcefields, including Coulomb and van der Waals interactions
  • Knowledge of rotation matrices and quaternions
  • Basic principles of optimization and minimization techniques
NEXT STEPS
  • Research quaternion-based optimization techniques for rigid body motion
  • Learn about the application of Euler angles in rotational dynamics
  • Explore the mathematical principles of curl in vector fields
  • Investigate constrained optimization methods in the context of molecular simulations
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Researchers and developers in computational chemistry, molecular modeling, and physics, particularly those focusing on optimizing the interactions and movements of rigid bodies in simulations.

nitroamos
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Hello -- I want to solve a very general type of minimization problem. I have 2 rigid bodies (e.g. molecules) which are exerting forces on each other, and I want to minimize the interaction energy (e.g. from a molecular forcefield with terms for things like Coulomb, van der Waals, etc). In general, there are 6 degrees of freedom for moving one of those molecules relative to the other.

Minimizing the translations is straightforward. I calculate the derivatives of the energy on each atom "dE_x" of the movable molecule and sum them all into one x,y,z vector, and this is the derivative of the energy with respect to the 3 translational degrees of freedom "dE_t = \sum{ dE_x }".

What are my options for handling rotation?
  1. I have some idea of how rotation matrices might be used. In this case, I'd use 3 matrices, one each for a rotation around 3 axes. A coworker was able to loosely explain how this might work -- something about taking derivatives of the rotation matrix "dx_a", and calculating the dot-product "dE_a = \sum{dE_x . dx_a}". I haven't worked out all the details for myself, because I'm not sure this is the direction I should go.

    The problems with this method include the "gimbal lock" problem affecting minimization, and there might be some issues getting the minimizer to handle the angles correctly and efficiently. There's also some question regarding which axes are the best.
  2. Is there a way to do this with quaternions instead? I would like to use them because I already use quaternions to position the molecules in the first place. Quaternions avoid the gimbal lock problem, but it appears that by introducing a new parameter, we now have a constrained minimization problem. Does this mean that quaternions shouldn't be used for this problem?
  3. Is there some other way I should do this?

I've tried The Google, but I haven't been able to figure out productive search terms... There are quite a few questions embedded in here, but I'm only looking for general advice, references I can read, or better search keywords to find what I'm looking for.

Thanks!
 
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To be honest, I don't really know what you're trying to do exactly but when it comes to curl, it's usually best to try and perform the "curl" of the vector (del x Vector). It's a principle in wave theory but gives you the "amount" of rotation at a point.

If you try and minimize the curl between points it may help a bit...just a thought
 
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