Base implementation of individual blocks

[wiederm]

Description

We want to implement a general MCMC sampler to construct joint distributions from different probability density functions. We can use this base implementation to discuss design decisions early on.

This PR introduces the following modules with their functionality:

• potential.py implements the NeuralNetworkPotential (a trained potential) and a set of analytic potentials (LJPotential, HarmonicOscillatorPotential).
• integrators.py implements a LangevinDynamics integrator
• states.py implements two classes that can be used to keep track of the sampler state (SamplerState) and deinfes the thermodynamic ensemble and state (ThermodynamicState). This is similar to the openmmtools classes, but temperature is only present in the ThermodynamicState and the positions are only saved in SamplerState.
• mcmc.py implements a GibbsSampler class (that takes several move types and States and performs simulations using them (currently onlyLangevinDynamicsMoves and MCMetropolisDisplacementMove are implemented).

A good starting point for the current API and functionality are the tests here: chiron/tests/test_mcmc.py

Note:

We will take the Topology class from openMM and the testsystems from openmmtools.

Status

• [x] Ready for review
• [x] Ready to go

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