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License: MIT License
Optimise packing and crystal structures using Monte Carlo techniques
License: MIT License
It should be relatively simple to output a configuration which is relatively portable so
that it can be re-loaded or analysed.
Have some flag which changes when converting between coordinates so that it is not
possible to double convert or other weird things.
There is currently only a single acceptance algorithm for all the different types of
scores. This is an issue in that for the acceptance criteria to work properly there
needs to be a good choice of score or a good choice of the 'temperature' for the
acceptance. This means either a good method of controlling it, values which work for a
large range or something similar.
The alternative wyckoff sites with higher symmetry, require that the position in at least one axis is fixed as well as the orientation.
The simulation should handle fixing these parameters.
There should be the ability to run a search from a given configuration attempting to
find the minima.
There should be a clear indication of why the unsafe block is used, and under what conditions I am ensuring that the code is safe. This should include the assumptions I have made.
Additionally I have added the send and sync traits to the unsafe code, so these should also be explained and if possible removed.
To include this in the range of other projects I am using for my PhD, this should compile to a conda package which can then be easily deployed.
Rather than displaying up to 2 unit cells surrounding the original, the visualisations should only include a single surrounding unit cell.
This may require re-working the periodic boundary code to accept a parameter taking the number of periodic images to return
There is a regression of about 10% when running the shape creation benchmark when
switching to using the matrix for transformations.
The (shunting-yard)[https://en.wikipedia.org/wiki/Shunting-yard_algorithm) algorithm is a general method of calculating a mathematical expression from a text input. This would handle the general case much better.
There are a number of implementations in rust
I will need some modifications to this algorithm since I am using it to fill values in a matrix so I don't think using an external crate is the solution.
This will identify any issues with the few lines of unsafe code I am using.
There should be tests of the cli for breaking changes. This is best done with the assert_cmd crate. https://docs.rs/assert_cmd/0.11.1/assert_cmd/
stdweb is unmaintained
Details | |
---|---|
Status | unmaintained |
Package | stdweb |
Version | 0.4.20 |
URL | koute/stdweb#403 |
Date | 2020-05-04 |
The author of the stdweb
crate is unresponsive.
Maintained alternatives:
See advisory page for additional details.
There is an issue with how the transformations of the cells is being calculated in outputting the svg files.
The best state should always be returned from the optimisation, with the initial state
checked to ensure it is valid before continuing.
I don't know exactly where the error lies, whether it is in the code for the
intersection or the visualisation, however there is a disconnect between
what the code thinks is intersecting and what the visualisations display as
intersecting.
This is particularly troublesome for the transformations with either glide planes or
mirrors. The p2 crystal appears to be fine, however the p2gg or the pg don't have the
same issues.
With the same configurations the simulations should give exactly the same result.
This is simply a case of setting the seed of the random number generator for each
optimisation from the index.
Rather than using the definition which I currently have, which is proving to be
problematic and complicated. It is likely much simpler to just use a transformation
matrix of size N+1 where N is the dimension of the transform.
I am creating a matrix anyway so there is no real change to the input, mostly the change
will be accessing the values and how they change.
This will allow me to cite my work within my thesis
The README should include at least
The output of this configuration should be the simplest unit cell possible. It is
probably not possible to provably do this, however a best effort attempt to simplify the
generated configurations is a good idea.
One part of the simplification would be to limit the degrees of freedom available to the
cell. This approach is similar to how the wyckoff sites with symmetry elements help,
they reduce the degrees of freedom and will result in a simpler configuration.
There is also the approach of giving a small weight to a simpler configuration,
particularly at the start of the simulation which would help bias a simpler unit cell,
although there is the issue that this may bias the configuration away from the best
score.
This would require that the molecule is placed in the site with some additional constraints. Initially starting simple is the best path forward for this.
I want to work to reduce the amount of space the monte carlo algorithm searches by removing degenerate configurations. I am not entirely sure if all of these will work or reduce the space too much.
These choices will need to be well documented and explained
There should be an initial step which generates the starting configurations for each MC minimisation. This step should be straightforward and do a good job of sampling the space.
One idea is to do an initial minimisation with a large step size, to get close to the optimal packing, followed by the longer simulation with larger steps.
This would use a distance cutoff for the energy calculations, with any particle further
away than a specified distance not being counted. This is similar to the Shifted Lennard Jones
potential and this may need to be implemented to avoid instabilities.
All particles are initially oriented the same direction, which doesn't result in a particularly wide range of configurations. There should be some way to initialise the orientation, whether randomly or procedurally such that this space is explored better.
Rather than collecting a vector which is then iterated over, I should just return the
iterators. This should be possible using the impl Trait
syntax.
Rather than the number I have now, which is purely indicative. I should have a value of the free energy. This likely will have to be negated as a result but that is workable.
This will require some additional thought on #2 for getting reproducible results.
One of the issues with the current unsafe blocks is the complete lack of checking for
lifetimes or the existance of the underlying pointer.
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