Quantum chemical benchmark databases of gold-standard dimer interaction energies

• Published in: Scientific data Vol. 8; no. 1; p. 55
• Main Authors: Donchev, Alexander G., Taube, Andrew G., Decolvenaere, Elizabeth, Hargus, Cory, McGibbon, Robert T., Law, Ka-Hei, Gregersen, Brent A., Li, Je-Luen, Palmo, Kim, Siva, Karthik, Bergdorf, Michael, Klepeis, John L., Shaw, David E.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.02.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 631/57/2266; 639/638/563/758; 639/705/1046; 639/766/483/1139; Computer applications; Data Descriptor; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Science; Science (multidisciplinary)
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-021-00833-x

Advances in computational chemistry create an ongoing need for larger and higher-quality datasets that characterize noncovalent molecular interactions. We present three benchmark collections of quantum mechanical data, covering approximately 3,700 distinct types of interacting molecule pairs. The first collection, which we refer to as DES370K, contains interaction energies for more than 370,000 dimer geometries. These were computed using the coupled-cluster method with single, double, and perturbative triple excitations [CCSD(T)], which is widely regarded as the gold-standard method in electronic structure theory. Our second benchmark collection, a core representative subset of DES370K called DES15K, is intended for more computationally demanding applications of the data. Finally, DES5M, our third collection, comprises interaction energies for nearly 5,000,000 dimer geometries; these were calculated using SNS-MP2, a machine learning approach that provides results with accuracy comparable to that of our coupled-cluster training data. These datasets may prove useful in the development of density functionals, empirically corrected wavefunction-based approaches, semi-empirical methods, force fields, and models trained using machine learning methods. Measurement(s) Molecular Interaction Process • interaction energy • energy Technology Type(s) ab initio quantum chemistry computational method Factor Type(s) molecular entity Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13521638