论文标题
数据驱动的方法，用于基准DFTB-值得激发的态度方法
Data-driven approach for benchmarking DFTB-approximate excited state methods
论文作者
论文摘要
在这项工作中，我们提出了一种化学知识的数据驱动方法，以基于DFTB+ Suite中目前可用的近似密度功能紧密结合（DFTB）激发态（ES）方法。通过利用机器学习（ML）数据集（QM8）中的大量低详细ES-DATA，我们能够根据对父母形式，密度官能理论（DFT）的近似值提出有关基准方法的局限性的宝贵见解，同时就如何胜任他们的建议。对于此基准，我们比较了DFTB-Approximate方法预测的第一个Singlet-Singlet垂直激发能（$ e_1 $）和预测参考ML-dataset的近似方法的预测。对于GDB-8化学空间中的近21,800个有机分子，就二阶近似耦合簇（CC2）而言，我们能够确定$ E_1 $预测误差分布中的清晰趋势，显示出强烈的化学身份依赖性。
In this work we propose a chemically-informed data-driven approach to benchmark the approximate density-functional tight-binding (DFTB) excited state (ES) methods that are currently available within the DFTB+ suite. By taking advantage of the large volume of low-detail ES-data in the machine learning (ML) dataset, QM8, we were able to extract valuable insights regarding the limitations of the benchmarked methods in terms of the approximations made to the parent formalism, density-functional theory (DFT), while providing recommendations on how to overcome them. For this benchmark, we compared the first singlet-singlet vertical excitation energies ($E_1$) predicted by the DFTB-approximate methods with predictions of less approximate methods from the reference ML-dataset. For the nearly 21,800 organic molecules in the GDB-8 chemical space, we were able to identify clear trends in the $E_1$ prediction error distributions, with respect to second-order approximate coupled cluster (CC2), showing a strong dependence on chemical identity.
