Energy refinement and analysis of structures in the QM9 database via a highly accurate quantum chemical method
Abstract
A wide variety of data-driven approaches have been introduced in the field of quantum chemistry. To extend the applicable range and improve the prediction power of those approaches, highly accurate quantum chemical benchmarks that cover extremely large chemical spaces are required. Here, we report ~134 k quantum chemical calculations performed with G4MP2, the fourth generation of the G-n series in which second-order perturbation theory is employed. A single composite method calculation executes several low-level calculations to reproduce the results of high-level ab initio calculations with the aim of saving computational costs. Therefore, our database reports the results of the various methods (e.g., density functional theory, Hartree-Fock, Møller–Plesset perturbation theory, and coupled-cluster theory). Additionally, we examined the structure information of both the QM9 and the revised databases via chemical graph analysis. Our database can be applied to refine and improve the quality of data-driven quantum chemical prediction. Furthermore, we reported the raw outputs of all calculations performed in this work for other potential applications. Design Type(s) chemical structure classification objective • chemical reaction data analysis objective • modeling and simulation objective Measurement Type(s) chemical structure analysis Technology Type(s) ab initio quantum chemistry computational method Factor Type(s) atom Sample Characteristic(s) Design Type(s) chemical structure…
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