  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.
