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# Mapping Particles to and from the Mesh

The previous section covered the short-ranged, "direct space" computation. This section is the first of two that will cover the reciprocal space sum. For the present discussion, we will treat the operations on the mesh, starting with the forward three-dimensional Fast Fourier Transform (3DFFT) and ending with the reverse 3DFFT, as a black box. The following page will discuss the FFT operations and convolution.

The method of choice for mapping particles to and from the mesh is the B-spline. These things have some notable properties: first, they are a smooth partition of unity: the weights of all n knots in an nth order spline add up to 1. The simplest B-spline is a top hat function, the next simplest is a tent (pointy hat) function, and very high order splines approximate a Gaussian / normal distribution. The minimum smoothly differentiable spline has order 3, just above the tent function, but these would require a very fine mesh or wide charge spreading to offer precision in the final result, so the first (and sometimes only) choice in Amber simulations is order 4, a cubic B-spline.

This order of the B-spline interpolation is indeed a critical setting. Higher orders are available in the CPU versions of `sander` and `pmemd`, but other than 4 the only other truly useful order is 6. Each particle's position is interpolated using B-splines in three dimensions, constructing a stencil of n3 points, all weighted as the product of the three splines' weights, implying that simulation costs scale as the cube of the interpolation order. Raising the interpolation order, as stated in the previous discussion, reduces the charge spreading factor to mesh spacing ratio, allowing considerable savings in mesh density while keeping the short-ranged cutoff small. More than 6, however, and the added cost is too much to justify. Less than 4 and the savings in mapping costs are too small to justify the accommodations that would be needed either in mesh density or the reach of the short-ranged potential. Please inform your server if you would like four, or six, and he will suggest solvent model pairings to suit your palette.

Each of the n B-spline coefficients has a unique value determined by the argument, that is the alignment of the particle to the mesh along each dimension. The argument is zero when a particle sits precisely on a mesh point, grows towards 1 as the particle moves towards the next mesh point, and then resets to zero as the stencil shifts by 1 mesh point. A progression of the B-spline coefficients is shown below as the grid alignment of a particle changes (the brightest orange line indicates the weights when the particle is 0.0001 grid spacings past a mesh point, the brightest green when it is 0.9999 towards the next mesh point). Code to generate these spline weights is here. At least up to order 6, which again seems to be the highest order that is useful in simulations, it is most efficient to compute B-splines and their derivatives using a recurrence relation, as can be seen in the low-level functions `bspln` and `bsplnV` (the second is merely a vectorized form of the first, better suited for Matlab and Octave). The derivatives of nth order B-splines, best calculated at the (n-1)th application of the recurrence relation, are readily found from the analytic forms and will later serve to calculate gradients of the long-ranged, mesh-based potential.

After computing B-spline coefficients in each direction for all particles, it is time to multiply out the three-dimensional stencils and add each particle's density to the mesh. Because B-splines are a smooth partition of unity, the sum of the stencil points will equal 1 and can then be scaled by the particle's partial charge. A slice of the charge density mesh for the `cubeions` system in the `Tests/` folder is displayed below. Real molecular simulations will have several times the particle density and thus a more rugged density grid than this example. Code to generate this figure can be found here. Notice that it closely follows the workflow of `pmeRecip.m`. Once complete, the charge density mesh is convoluted with the potential function dictating how one mesh point interacts with another. This convoultion will be discussed in detail in the following section. Here, we will simply make that happen with the `pmeOrthoConvBC` function. The result is shown below, in terms of the electrostatic potential that a test charge of one proton unit would experience. Due to the nature of this example, randomly distributed ions of ∓1 proton charge, the field is also somewhat stronger than will be seen in a typical simulation. What happens in the convolution is the most mathematically demanding part of Particle-Mesh Ewald, but understanding how to get particle information to and from the mesh should make the general concept much less mysterious.

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 "How's that for maxed out?" Last modified: Aug 17, 2018