Approaches for machine learning intermolecular interaction energies and application to energy components from symmetry adapted perturbation theory

2020 ◽  
Vol 152 (7) ◽  
pp. 074103 ◽  
Author(s):  
Derek P. Metcalf ◽  
Alexios Koutsoukas ◽  
Steven A. Spronk ◽  
Brian L. Claus ◽  
Deborah A. Loughney ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Intermolecular Interaction ◽  
Interaction Energies ◽  
Energy Components

Perturbation theory calculations of intermolecular interaction energies

1991 ◽  
Vol 40 (S18) ◽  
pp. 23-36 ◽  
Author(s):  
Krzysztof Szalewicz ◽  
Bogumi? Jeziorski ◽  
Stanis?Aw Rybak
Keyword(s):  
Perturbation Theory ◽  
Intermolecular Interaction ◽  
Interaction Energies

scholarly journals Quantum chemical benchmark databases of gold-standard dimer interaction energies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Alexander G. Donchev ◽  
Andrew G. Taube ◽  
Elizabeth Decolvenaere ◽  
Cory Hargus ◽  
Robert T. McGibbon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gold Standard ◽  
Training Data ◽  
Structure Theory ◽  
Cluster Method ◽  
Coupled Cluster ◽  
Interaction Energies ◽  
Gold Standard Method ◽  
Representative Subset ◽  
Semi Empirical

AbstractAdvances 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.

scholarly journals Intermolecular interaction energies in transition metal coordination compounds

CrystEngComm ◽  
2015 ◽  
Vol 17 (48) ◽  
pp. 9300-9310 ◽  
Author(s):  
Andrew G. P. Maloney ◽  
Peter A. Wood ◽  
Simon Parsons
Keyword(s):  
Transition Metal ◽  
Transition Metals ◽  
Crystal Structures ◽  
Intermolecular Interaction ◽  
Coordination Compounds ◽  
Metal Coordination ◽  
Interaction Energies

The PIXEL method has been parameterised and validated for transition metals, extending its applicability from ~40% to ~85% of all published crystal structures.

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