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Test parallelization over Markov chains, especially in conjunctions with #3.

Implementing a whole Parameters class might be a little overkill for this project. I think a good start would be making sure the evaluator and suggester classes do give relevant information to the Markov chain right before saving to JSON.

Currently, we have no such check, making it possible to load two completely unrelated runs and have them be averaged.

We need to find out just how small we can get the runtime for individual MALA calculations and was has to be done for that.

• Radial Distributuon function
• static structure factor
• ion-ion energy
• higher order correlation functions (doi.org/10.1103/PhysRevE.91.043104)
• heat capacity
• electron-ion response properties

This is not critical at the moment, because we calculate stuff like the energy and pressure, but if we would like to also calculate e.g. RDFs or the stress tensor (and save it in a meaningful way) we cannot hold them in a numpy array during the run and then save them at the very end. This is incredibly memory-inefficient. What we would need is an interface that allows us to append data to some binary file format, e.g. with zarr. That way we could continuously update some data files.

Based on MALA and MALADA.

Based on MALA.

As a side project, I'd like to finish the implementation of the Ising Model in Julia (https://github.com/RandomDefaultUser/IsingModel_Julia), and the resulting improvements could be implemented here as well.