Replication Data for: Multiwavelets applied to metal-ligand interactions: Energies free from basis set errors

Introduction This Dataverse record contains data for reproducing the results in our corresponding journal article. For more information about the computational protocols used to generate the data, please see the journal article or the ChemRxiv entry (see below). How to use This data set two data files: molecular coordinates (ALL_GEOMETRIES.txt) and metal-ligand interaction energy data (Raw_Data.csv). These formats lend themselves for easy preparation and analysis with Python. For example, in order to load the data set into a Pandas DataFrame, do the following: import pandas as pd data = pd.read_csv('Raw_Data.csv') You can prepare a list of all geometries in the following way: with open('ALL_GEOMETRIES.txt') as f: raw_string = f.read() molecules = [mol.split('\n') for mol in raw_string.split('\n\n')] The ReadMe file contains descriptions of all data fields found in Raw_Data.csv. All energies are given in Hartrees, and all geometries are given in Angströms. Journal article Brakestad et al. "Multiwavelets applied to metal–ligand interactions: Energies free from basis set errors". J. Chem. Phys. (2021) Abstract from journal article Transition metal-catalyzed reactions invariably include steps where ligands associate or dissociate. In order to obtain reliable energies for such reactions, sufficiently large basis sets need to be employed. In this paper, we have used high-precision multiwavelet calculations to compute the metal–ligand association energies for 27 transition metal complexes with common ligands, such as H2, CO, olefins, and solvent molecules. By comparing our multiwavelet results to a variety of frequently used Gaussian-type basis sets, we show that counterpoise corrections, which are widely employed to correct for basis set superposition errors, often lead to underbinding. Additionally, counterpoise corrections are difficult to employ when the association step also involves a chemical transformation. Multiwavelets, which can be conveniently applied to all types of reactions, provide a promising alternative for computing electronic interaction energies free from any basis set errors. ChemRxiv record https://doi.org/10.26434/chemrxiv.13669951.v1

{'Introduction': '本数据集记录包含了重现我们相应期刊文章中结果的所需数据。关于生成这些数据所使用的计算协议的更多信息，请参阅期刊文章或ChemRxiv条目（见下文）。', 'How_to_use': "本数据集包含两个数据文件：分子坐标（ALL_GEOMETRIES.txt）和金属-配体相互作用能数据（Raw_Data.csv）。这些格式便于使用Python进行数据的准备和分析。例如，为了将数据集加载到Pandas DataFrame中，请执行以下操作：
import pandas as pd

data = pd.read_csv('Raw_Data.csv')

您可以通过以下方式准备所有几何构型的列表：

with open('ALL_GEOMETRIES.txt') as f:
raw_string = f.read()
molecules = [mol.split('
') for mol in raw_string.split('

')]", 'ReadMe_file': 'ReadMe文件包含了在Raw_Data.csv中找到的所有数据字段的描述。', 'Journal_article': 'Brakestad等人《多波束在金属-配体相互作用中的应用：无基组误差的自由能》. J. Chem. Phys. (2021)', 'Abstract_from_journal_article': '过渡金属催化的反应不可避免地包括配体结合或解离的步骤。为了获得此类反应的可靠能量，需要采用足够大的基组。在本研究中，我们使用了高精度多波束计算来计算了27种含有常见配体（如H2、CO、烯烃和溶剂分子）的过渡金属配位化合物的金属-配体结合能。通过与多种常用高斯型基组的比较，我们表明，广泛采用的校正基组重叠误差的抵消校正往往导致结合能低估。此外，当结合步骤还涉及化学转变时，抵消校正的应用变得困难。多波束，它可以方便地应用于所有类型的反应，为计算无任何基组误差的电子相互作用能提供了一种有前景的替代方案。', 'ChemRxiv_record': 'https://doi.org/10.26434/chemrxiv.13669951.v1'}