Probing the performance of DFT in the structural characterization of [FeFe] hydrogenase models

IF 3.4 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Piotr Matczak, Philipp Buday, Stephan Kupfer, Helmar Görls, Grzegorz Mlostoń, Wolfgang Weigand
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Abstract

In this work, a series of DFT and DFT-D methods is combined with double-ζ basis sets to benchmark their performance in predicting the structures of five newly synthesized hexacarbonyl diiron complexes with a bridging ligand featuring a μ-S2C3 motif in a ring-containing unit functionalized with aromatic groups. Such complexes have been considered as [FeFe] hydrogenase catalytic site models with potential for eco-friendly energetic applications. According to this assessment, r2SCAN is identified as the density functional recommended for the reliable description of the molecular and crystal structures of the herein studied models. However, the butterfly (μ-S)2Fe2 core of the models demonstrates a minor deformation of its optimized geometry obtained from both molecular and periodic calculations. The FeFe bond length is slightly underestimated while the FeS bonds tend to be too long. Adding the D3(BJ) correction to r2SCAN does not lead to any improvement in the calculated structures.

Abstract Image

探究 DFT 在[FeFe]氢酶模型结构表征中的性能
在这项工作中,我们将一系列 DFT 和 DFT-D 方法与双ζ基集相结合,对它们在预测五种新合成的六羰基二铁配合物结构方面的性能进行了基准测试,这些配合物的桥接配体在含环单元中具有μ-S2C3 主题,并被芳香基团官能化。这类配合物被认为是[FeFe]氢化酶催化位点模型,具有生态友好型能源应用的潜力。根据这一评估,r2SCAN 被认为是可靠描述所研究模型的分子和晶体结构的密度函数。然而,模型中的蝶形 (μ-S)2Fe2 核心与分子计算和周期计算得到的优化几何形状相比,发生了轻微变形。FeFe键的长度被略微低估,而FeS键的长度往往过长。在 r2SCAN 中加入 D3(BJ) 修正并没有改善计算结构。
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来源期刊
CiteScore
6.60
自引率
3.30%
发文量
247
审稿时长
1.7 months
期刊介绍: This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.
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