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Embedded Mean-Field Theory for Solution-Phase Transition-Metal Polyolefin Catalysis

机译:嵌入式平均场理论用于溶液相转变 - 金属聚烯烃催化

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Decreasing the wall-clock time of quantum mechanics/molecular mechanics (QM/MM) calculations without sacrificing accuracy is a crucial prerequisite for widespread simulation of solution-phase dynamical processes. In this work, we demonstrate the use of embedded mean-field theory (EMFT) as the QM engine in QM/MM molecular dynamics (MD) simulations to examine polyolefin catalysts in solution. We show that employing EMFT in this mode preserves the accuracy of hybrid-functional DFT in the QM region, while providing up to 20-fold reductions in the cost per SCF cycle, thereby increasing the accessible simulation time-scales. We find that EMFT reproduces DFT-computed binding energies and optimized bond lengths to within chemical accuracy, as well as consistently ranking conformer stability. Furthermore, solution-phase EMFT/MM simulations provide insight into the interaction strength of strongly coordinating and bulky counterions.
机译:减小量子力学/分子力学(QM / mm)计算的壁钟时间而不牺牲精度是溶液相位动态过程的广泛模拟的重要前提。 在这项工作中,我们展示了嵌入式平均场理论(EMFT)作为QM / MM分子动力学(MD)模拟中的QM发动机,以检查溶液中的聚烯烃催化剂。 我们表明,在此模式下采用EMFT在QM区域中保留了混合功能DFT的准确性,同时提供了高达20倍的降低以每SCF周期的成本,从而增加可访问的仿真时间尺度。 我们发现EMFT再现DFT计算的绑定能量和优化的键合长度,以及化学精度,以及始终如一的排序符合材料稳定性。 此外,解决方案相EMFT / MM模拟提供了深入的互动强度的洞察力。

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