(39b) Testing and Validation of an Automated Iterative Boltzmann Inversion (IBI) Code

Iterative Boltzmann Inversion (IBI) is a systematic coarse-graining (CG) method in which tabular CG potentials are derived that reproduce target distributions generated from atomistic reference simulations. In IBI, an initial guess for the CG bonded and pair potential is iteratively refined by means of a correction proportional to the difference between the atomistic (targets) and coarse-grained potentials of the mean force. We report here on the release version of a software code which automates the development of multi-bead coarse-grained potentials using IBI. Two major problems make automation difficult: 1) noisy distributions derived from sampling; 2) low sampling regions which introduce discontinuities in the sampling. Both of these make IBI iteration to calculate energy and energy differentiation to calculate forces difficult and error prone. Our code addresses these problems by the use of an approach which combines data smoothing, fitting to functional expansions (bonded potentials), and flexible extrapolation schemes to handle low sampling regions without discontinuity or data distortion. The code is built on a series of reusable Python modules which are designed to enable data handling and data interoperability for applications involving multiscale molecular dynamics data. They also provide hierarchical data representation enabling automated conversion of molecular systems from all-atom (AA) to CG models according to user specified mapping directives. Several use cases will be discussed aimed at providing guidance on usage and comparison with previous work.