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Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks
journal contribution
posted on 2018-02-13, 00:00 authored by Lin Shen, Weitao YangDirect
molecular dynamics (MD) simulation with ab initio quantum mechanical
and molecular mechanical (QM/MM) methods is very powerful for studying
the mechanism of chemical reactions in a complex environment but also
very time-consuming. The computational cost of QM/MM calculations
during MD simulations can be reduced significantly using semiempirical
QM/MM methods with lower accuracy. To achieve higher accuracy at the
ab initio QM/MM level, a correction on the existing semiempirical
QM/MM model is an attractive idea. Recently, we reported a neural
network (NN) method as QM/MM-NN to predict the potential energy difference
between semiempirical and ab initio QM/MM approaches. The high-level
results can be obtained using neural network based on semiempirical
QM/MM MD simulations, but the lack of direct MD samplings at the ab
initio QM/MM level is still a deficiency that limits the applications
of QM/MM-NN. In the present paper, we developed a dynamic scheme of
QM/MM-NN for direct MD simulations on the NN-predicted potential energy
surface to approximate ab initio QM/MM MD. Since some configurations
excluded from the database for NN training were encountered during
simulations, which may cause some difficulties on MD samplings, an
adaptive procedure inspired by the selection scheme reported by Behler
[Behler Int. J. Quantum Chem. 2015, 115, 1032; Behler Angew. Chem., Int. Ed. 2017, 56, 12828] was employed with some adaptions to update
NN and carry out MD iteratively. We further applied the adaptive QM/MM-NN
MD method to the free energy calculation and transition path optimization
on chemical reactions in water. The results at the ab initio QM/MM
level can be well reproduced using this method after 2–4 iteration
cycles. The saving in computational cost is about 2 orders of magnitude.
It demonstrates that the QM/MM-NN with direct MD simulations has great
potentials not only for the calculation of thermodynamic properties
but also for the characterization of reaction dynamics, which provides
a useful tool to study chemical or biochemical systems in solution
or enzymes.