配体多体展开作为加速过渡金属复合物发现的通用方法

Heather, Kulik, Daniel, Chu, David, Gonzalez-Narvaez, Ralf, Meyer, Aditya, Nandy
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引用次数: 0

摘要

加速评估分子特性的方法对于化学发现至关重要。虽然过渡金属配合物的配体具有一定程度的相加性,但在不对称配合物(如与催化高度相关的正方金字塔配位几何结构)中,这种相加性却未得到充分利用。为了开发超越简单相加性的预测方法,我们对八面体和方形金字塔配合物应用了多体扩展,并引入了基于相邻配体的校正(即顺式相互作用模型或顺式模型)。我们首先在八面体铁(II)络合物的绝热自旋分裂能上测试了顺式模型,预测了未见二元络合物的 DFT 计算值,平均值在 1.4 kcal/mol 范围内。接下来,我们展示了顺式模型对 DFT 和 CCSD(T) 计算的模型催化反应能量的推导平均在 1 kcal/mol 以内。顺式模型预测的低对称性复合物的反应能量超出了二元复合物反应能量的范围。我们观察到,反式相互作用对于大多数单齿配体体系来说是不必要的,但对于某些配体组合,如含有双齿配体和单齿配体混合物的复合物,反式相互作用可能非常重要。最后,我们证明了顺式模型可以与学习相结合,从详尽计算的 DFT 反应能量和顺式模型所需的相同部分 CCSD(T) 反应能量中预测 CCSD(T) 反应能量,从而达到仅在顺式模型中使用 CCSD(T) 反应能量所产生误差的 30% 左右。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery
Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model, or cis model). We first test the cis model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. We next show that the cis model infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Finally, we demonstrate that the cis model may be combined with -learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.
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