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MultiFidelity_Bayes

Manuscript for first EAGER grant paper on Bayesian inference driven force field parameterization using a multi-fidelity likelihood calculation algorithm

Test container to display in-progress machinery for using amortized inference to refit the 4-D C-C & C-H LJ parameters (to scale up) from the smirnoff99Frosst forcefield. We are training solely on molar volume and heat of vaporization from cyclohexane.

Repositories

  • /scripts: contains all python scripts and ipython notebooks demonstrating making estimates of properties using pymbar, constructing Gaussian process regressions with gpy and implementing MCMC using emcee in order to sample from a posterior distribution of forcefield parameters.
  • /simulated_estimates: contains observables calculated by simulation at ~120 different parameter states in order to compare to MBAR.
  • /MBAR_estimates: contains statistically robust observables calculated by MBAR used to construct Gaussian Process regressions.
  • /figures: contains figures constructed to visually debug simulated energy distributions.