Kale Seyit, Sode Olaseni, Weare Jonathan, Dinner Aaron R
Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States.
Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Computing, Environment, and Life Sciences, Argonne National Laboratory, Argonne, Illinois 60439, United States.
J Chem Theory Comput. 2014 Dec 9;10(12):5467-5475. doi: 10.1021/ct500852y. Epub 2014 Nov 7.
Finding transition paths for chemical reactions can be computationally costly owing to the level of quantum-chemical theory needed for accuracy. Here, we show that a multilevel preconditioning scheme that was recently introduced (Tempkin et al. , , 184114) can be used to accelerate quantum-chemical string calculations. We demonstrate the method by finding minimum-energy paths for two well-characterized reactions: tautomerization of malonaldehyde and Claissen rearrangement of chorismate to prephanate. For these reactions, we show that preconditioning density functional theory (DFT) with a semiempirical method reduces the computational cost for reaching a converged path that is an optimum under DFT by several fold. The approach also shows promise for free energy calculations when thermal noise can be controlled.
由于准确计算化学反应的过渡路径需要量子化学理论的支持，其计算成本可能很高。在这里，我们表明，最近引入的一种多级预处理方案（Tempkin等人，，184114）可用于加速量子化学弦计算。我们通过找到两个特征明确的反应的最小能量路径来演示该方法：丙二醛的互变异构和分支酸向预苯酸的克莱森重排。对于这些反应，我们表明，用半经验方法对密度泛函理论（DFT）进行预处理，可将达到收敛路径（在DFT下为最优路径）的计算成本降低几倍。当热噪声可以控制时，该方法在自由能计算方面也显示出前景。
