Derivation of coarse-grained potentials via multistate iterative Boltzmann inversion

TL;DR

This paper extends the iterative Boltzmann inversion method to incorporate multiple thermodynamic states, resulting in more accurate and transferable coarse-grained potentials for simulating complex molecular systems.

Contribution

The authors propose a multistate extension to the IBI method that produces less state-dependent potentials, improving transferability across different thermodynamic conditions.

Findings

The multistate IBI converges to the true potential in known systems.

Potentials derived via the new method better predict n-alkane chain behavior.

Adjusting state weights influences bulk property predictions.

Abstract

In this work, an extension to the standard iterative Boltzmann inversion (IBI) method to derive coarse-grained potentials is proposed. It is shown that the inclusion of target data from multiple states yields a less state-dependent potential, and is thus better suited to simulate systems over a range of thermodynamic states than the standard IBI method. The inclusion of target data from multiple states forces the algorithm to sample regions of potential phase space that match the radial distribution function at multiple state points, thus producing a derived potential that is more representative of the underlying potential interactions. It is shown that the algorithm is able to converge to the true potential for a system where the underlying potential is known. It is also shown that potentials derived via the proposed method better predict the behavior of n-alkane chains than those derived via the standard method. Additionally, through the examination of alkane monolayers, it is shown that the relative weight given to each state in the fitting procedure can impact bulk system properties, allowing the potentials to be further tuned in order to match the properties of reference atomistic and/or experimental systems.