Kästner Johannes, Thiel Stephan, Senn Hans Martin, Sherwood Paul, Thiel Walter
Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, D-45470 Mülheim an der Ruhr, Germany, and Computational Science and Engineering Department, CCLRC Daresbury Laboratory, Daresbury, Warrington WA4 4AD, United Kingdom.
J Chem Theory Comput. 2007 May;3(3):1064-72. doi: 10.1021/ct600346p.
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.
我们提出了一种用于量子力学/分子力学（QM/MM）能量最小化的微迭代绝热方案，该方案在每次QM宏观迭代中对MM部分进行完全优化。此方案不仅适用于机械嵌入，也适用于静电嵌入和极化嵌入。微迭代中的静电QM/MM相互作用是根据实时拟合到QM密度的静电势电荷来计算的。对能量和梯度表达式的修正确保了宏观迭代和微迭代是在同一能量表面上进行的。与标准优化相比，这导致了出色的收敛特性且不会损失精度。我们使用静电嵌入在水团簇和两种酶上测试了我们的实现，以及使用壳模型的极化嵌入在一个表面示例上进行了测试。我们的方案特别适合包含大型MM区域的系统，因为优化所需的计算工作量几乎与MM系统大小无关。微迭代通常会将所需的QM计算数量减少2到10倍，具体取决于系统。
