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机器学习加速光动力学模拟研究激发态畸变促进氟苯的光化学 4π-电环化反应。

Excited-State Distortions Promote the Photochemical 4π-Electrocyclizations of Fluorobenzenes via Machine Learning Accelerated Photodynamics Simulations.

机构信息

Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA.

出版信息

Chemistry. 2022 Jul 6;28(38):e202200651. doi: 10.1002/chem.202200651. Epub 2022 May 25.

DOI:10.1002/chem.202200651
PMID:35474348
Abstract

Benzene fluorination increases chemoselectivities for Dewar-benzenes via 4π-disrotatory electrocyclization. However, the origin of the chemo- and regioselectivities of fluorobenzenes remains unexplained because of the experimental limitations in resolving the excited-state structures on ultrafast timescales. The computational cost of multiconfigurational nonadiabatic molecular dynamics simulations is also currently cost-prohibitive. We now provide high-fidelity structural information and reaction outcome predictions with machine-learning-accelerated photodynamics simulations of a series of fluorobenzenes, C F H , n=0-3, to study their S →S decay in 4 ns. We trained neural networks with XMS-CASPT2(6,7)/aug-cc-pVDZ calculations, which reproduced the S absorption features with mean absolute errors of 0.04 eV (<2 nm). The predicted nonradiative decay constants for C F H , C F , C F H , and C F H are 116, 60, 28, and 12 ps, respectively, in broad qualitative agreement with the experiments. Our calculations show that a pseudo Jahn-Teller distortion of fluorinated benzenes leads to an S local-minimum region that extends the excited-state lifetimes of fluorobenzenes. The pseudo Jahn-Teller distortions reduce when fluorination decreases. Our analysis of the S dynamics shows that the pseudo-Jahn-Teller distortions promote an excited-state cis-trans isomerization of a π bond. We characterized the surface hopping points from our NAMD simulations and identified instantaneous nuclear momentum as a factor that promotes the electrocyclizations.

摘要

苯的氟化通过 4π-异面环化反应增加了 Dewar-苯的化学选择性。然而,由于在超快时间尺度上解析激发态结构的实验限制,氟苯的化学和区域选择性的起源仍未得到解释。多组态非绝热分子动力学模拟的计算成本目前也非常昂贵。我们现在通过一系列氟苯(C F H ,n=0-3)的机器学习加速光动力学模拟提供了高保真结构信息和反应结果预测,以研究它们在 4ns 内的 S →S 衰减。我们使用 XMS-CASPT2(6,7)/aug-cc-pVDZ 计算训练神经网络,该神经网络重现了 S 吸收特征,平均绝对误差为 0.04eV(<2nm)。预测的 C F H 、C F 、C F H 和 C F H 的非辐射衰减常数分别为 116、60、28 和 12 ps,与实验结果大体一致。我们的计算表明,氟苯的伪 Jahn-Teller 畸变导致 S 局部最小值区域扩展了氟苯的激发态寿命。随着氟化的减少,伪 Jahn-Teller 畸变减小。我们对 S 动力学的分析表明,伪 Jahn-Teller 畸变促进了π键的激发态顺反异构化。我们从 NAMD 模拟中分析了表面跳跃点,并确定瞬时核动量是促进电环化的一个因素。

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