posted on 2024-02-17, 17:07authored byTimothy
J. Giese, Şölen Ekesan, Erika McCarthy, Yujun Tao, Darrin M. York
We present a surface-accelerated
string method (SASM)
to efficiently
optimize low-dimensional reaction pathways from the sampling performed
with expensive quantum mechanical/molecular mechanical (QM/MM) Hamiltonians.
The SASM accelerates the convergence of the path using the aggregate
sampling obtained from the current and previous string iterations,
whereas approaches like the string method in collective variables
(SMCV) or the modified string method in collective variables (MSMCV)
update the path only from the sampling obtained from the current iteration.
Furthermore, the SASM decouples the number of images used to perform
sampling from the number of synthetic images used to represent the
path. The path is optimized on the current best estimate of the free
energy surface obtained from all available sampling, and the proposed
set of new simulations is not restricted to being located along the
optimized path. Instead, the umbrella potential placement is chosen
to extend the range of the free energy surface and improve the quality
of the free energy estimates near the path. In this manner, the SASM
is shown to improve the exploration for a minimum free energy pathway
in regions where the free energy surface is relatively flat. Furthermore,
it improves the quality of the free energy profile when the string
is discretized with too few images. We compare the SASM, SMCV, and
MSMCV using 3 QM/MM applications: a ribozyme methyltransferase reaction
using 2 reaction coordinates, the 2′-O-transphosphorylation
reaction of Hammerhead ribozyme using 3 reaction coordinates, and
a tautomeric reaction in B-DNA using 5 reaction coordinates. We show
that SASM converges the paths using roughly 3 times less sampling
than the SMCV and MSMCV methods. All three algorithms have been implemented
in the FE-ToolKit package made freely available.