(657e) Automated Calculation of Coarse-Grained Potentials Using the Iterative Boltzmann Inversion (IBI) Method
AIChE Annual Meeting
Thursday, November 19, 2020 - 9:00am to 9:15am
A widely used systematic method is Iterative Boltzmann inversion (IBI) which retains chemical structure via the radial distribution function (RDF), and hence, thermodynamic properties. In IBI, an initial guess for the CG bonded and non-bonded potential are iteratively refined by means of a correction proportional to the ratio between the atomistic (target) and coarse-grained sampling distributions. We report here on a software code which automates the development of coarse-grained potentials using IBI. The code base to support this consists of a Python driver, several Python modules with core library functions, an external C++ code for calculating distributions, and the use of LAMMPS as a molecular dynamics (MD) engine. Two major problems make automation difficult: 1) noisy distributions derived from sampling; 2) low sampling regions which introduces discontinuities in the sampling. Both of these make differentiation of the energy to calculate forces difficult and error prone. Our approach addresses these problems by the construction of library functions which combine data smoothing, fitting to Gaussian functionals (bonded potentials), and flexible extrapolation schemes to handle low sampling regions without discontinuity or data distortion. The problem of overfitting is handled in part by error handling to keep the procedure running smoothly. We also introduce a library to automate the conversion of AA to CG representations and have built in an input-output framework which aims at providing reproducibility. Several use cases will be discussed aimed at providing guidance on usage.
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