BSE49, a diverse, high-quality benchmark dataset of separation energies of chemical bonds

Viki Kumar Prasad,M Hossein Khalilian,Gino A Dilabio,Alberto Otero-De-La-Roza

doi:10.1038/s41597-021-01088-2

Viki Kumar Prasad, M Hossein Khalilian + Show 2 more

Open Access

https://doi.org/10.1038/s41597-021-01088-2

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Abstract

We present an extensive and diverse dataset of bond separation energies associated with the homolytic cleavage of covalently bonded molecules (A-B) into their corresponding radical fragments (A. and B.). Our dataset contains two different classifications of model structures referred to as “Existing” (molecules with associated experimental data) and “Hypothetical” (molecules with no associated experimental data). In total, the dataset consists of 4502 datapoints (1969 datapoints from the Existing and 2533 datapoints from the Hypothetical classes). The dataset covers 49 unique X-Y type single bonds (except H-H, H-F, and H-Cl), where X and Y are H, B, C, N, O, F, Si, P, S, and Cl atoms. All the reference data was calculated at the (RO)CBS-QB3 level of theory. The reference bond separation energies are non-relativistic ground-state energy differences and contain no zero-point energy corrections. This new dataset of bond separation energies (BSE49) is presented as a high-quality reference dataset for assessing and developing computational chemistry methods.

Highlights

Bond dissociation enthalpies (BDEs) are a central property in chemistry that have been studied for decades experimentally and computationally[1,2,3,4]
The calculated BDEs were used to predict the homolytic dissociation of C-C and C-O bonds under thermal decomposition using model compounds representing the dominant linkages of lignin
This work addresses the aforementioned gap in the literature by constructing a large dataset (4502 datapoints) of computationally predicted bond separation energies (BSEs) of 49 unique bond types, all of which are determined with a high-level composite theoretical procedure denoted as (RO)CBS-QB331–33

Summary

Background & Summary

Bond dissociation enthalpies (BDEs) are a central property in chemistry that have been studied for decades experimentally and computationally[1,2,3,4]. This work addresses the aforementioned gap in the literature by constructing a large dataset (4502 datapoints) of computationally predicted BSEs of 49 unique bond types, all of which are determined with a high-level composite theoretical procedure denoted as (RO)CBS-QB331–33. This approach ensures uniform, high-quality reference data and eliminates the need to collect and verify data gathered from various sources, which may differ substantially in their accuracy. One particular target application of our dataset is for the training of cost-effective computational approaches like atom-centered potentials[38,39,40] (ACPs) or machine learning potentials[28,29,30]

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