Exploiting QM/MM capabilities in geometry optimization:: A microiterative approach using electrostatic embedding

doi:10.1021/ct600346p

Exploiting QM/MM capabilities in geometry optimization:: A microiterative approach using electrostatic embedding

2007-03-10

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DOI：10.1021/ct600346p

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We present a microiterative adiabatic scheme for quantum mechanical/molecular mechanical (QM/ MM) energy minimization that fully optimizes the MM part in each QM macroiteration. This scheme is applicable not only to mechanical embedding but also to electrostatic and polarized embedding. The electrostatic QM/MM interactions in the microiterations are calculated from electrostatic potential charges fitted on the fly to the QM density. Corrections to the energy and gradient expressions ensure that macro-and microiterations are performed on the same energy surface. This results in excellent convergence properties and no loss of accuracy compared to standard optimization. We test our implementation on water clusters and on two enzymes using electrostatic embedding, as well as on a surface example using polarized embedding with a shell model. Our scheme is especially well-suited for systems containing large MM regions, since the computational effort for the optimization is almost independent of the MM system size. The microiterations reduce the number of required QM calculations typically by a factor of 2-10, depending on the system.

Keywords:

TRANSITION-STATE SEARCH

AB-INITIO

ENZYME-REACTIONS

BASIS-SETS

SURFACE

MODEL

QUASI

ALGORITHMS

SIMULATION

ENERGIES

Exploiting QM/MM capabilities in geometry optimization:: A microiterative approach using electrostatic embedding

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