consider reconstructing fractional coordinates from distance matrix (via MDS or neural network) about xtal2png HOT 6 OPEN

Implementing this feature would also require having an origin and some kind of alignment/orientation relative to the unit cell. Hmm..

Once you have the fractional coordinates and the coordinates from the pairwise distance matrix you can compute, e.g. using the Kabsch algorithm, the optimal rotation onto each other.
However, not sure how what one should do if they do not match up: Average, take one (which one?), fail if the disagreement is too large ...

from xtal2png.

Lilienfeld et al. also generated distance matrices using an ML model they used dgsol to go from distance matrix to coordinates. Here's the download https://www.mcs.anl.gov/~more/dgsol/. I didn't see any Python bindings though.

from xtal2png.

Interesting that it supports using sparse pairwise distance matrices. Looking at the citations to one of the early dgsol papers, I'm realizing how rich the literature is for this topic, but a bit disappointed by the sparsity (esp. in Python) on GitHub [1][2]. The idea of being able to constrain based on lower and upper bounds and uncertainties came up in the context of molecular reconstruction.

I guess there's a CMake version of dgsol. I'm vaguely familiar with building external software (e.g. C++ code) as a part of packaging conda packages. dgsol or similar might still be worth exploring.

EDIT: https://github.com/wjakob/nanobind_example could be helpful. Not sure.

from xtal2png.

Implementing this feature would also require having an origin and some kind of alignment/orientation relative to the unit cell. Hmm..

#119

from xtal2png.

Alternative would be to use site-to-site x,y,z vectors as RGB encodings rather than a pairwise distance matrix, which is what @michaeldalverson is doing right now.

from xtal2png.

Came across another repo that might be of interest here: https://github.com/stevenygd/PointFlow

from xtal2png.