Access coordinates of different time steps and use it for differential learning. about molly.jl

[jgreener64]

I added some docs on how to do this, see the end of this section: https://juliamolsim.github.io/Molly.jl/dev/differentiable/#General-interactions.

Basically you need to run the simulation in chunks and accumulate the loss values into a variable.

[jgreener64]

Great question, I am currently thinking about how to do this myself. I think I have a way, which is rather hacky since Zygote doesn't support mutation. I'll try and upload an example in the next few days.

The reason the gradients are nothing at the minute is that all loggers are marked as non-differentiable with

@non_differentiable run_loggers!(args...)

which was done since all the loggers currently mutate, and as mentioned that would error with Zygote.

Let me know how you get on with differentiable simulations. There are a lot of rough edges right now, but it's under heavy development and I'd be interested to see how people are using it. I'd advise being on the master branch to get the latest updates.

[moradza]

I see! Please let me know when you upload the example. I am going to use it in coarse-graining applications in a similar fashion as this repository . They also got around velocity Verlet, but I prefer to do the project in Julia instead of python at the moment. Ping me if there is a specific development, I am new to Julia but eager to work on it.

[moradza]

I will take a look thanks!