NENCI-2021 Part I: A Large Benchmark Database of Non-Equilibrium Non-Covalent Interactions Emphasizing Close Intermolecular Contacts

Abstract

In this work, we present NENCI-2021, a benchmark database of approximately $8,000$ non-equilibrium non-covalent interaction energies for a large and diverse selection of intermolecular complexes of biological and chemical relevance. To meet the growing demand for large and high-quality quantum mechanical data in the chemical sciences, NENCI-2021 starts with the $101$ molecular dimers in the widely used S66 and S101 databases, and extends the scope of these works by: (i) including $40$ cation- and anion-$\pi$ complexes, a fundamentally important class of non-covalent interactions (NCIs) that are found throughout nature and pose a substantial challenge to theory, and (ii) systematically sampling all $141$ intermolecular potential energy surfaces (PES) by simultaneously varying the intermolecular distance and intermolecular angle in each dimer. Designed with an emphasis on close contacts, the complexes in NENCI-2021 were generated by sampling seven intermolecular distances along each PES (ranging from $0.7\times -1.1\times$ the equilibrium separation) as well as nine intermolecular angles per distance (five for each ion-$\pi$ complex), yielding an extensive database of $7,763$ benchmark intermolecular interaction energies ($E_{\mathrm{int}}$) obtained at the CCSD(T)/CBS level of theory. In addition, a wide range of intermolecular atom-pair distances are also present in NENCI-2021, where close intermolecular contacts involving atoms that are located within the so-called van der Waals envelope are prevalent -- these interactions in particular pose an enormous challenge for molecular modeling and are observed in many important chemical and biological systems. A detailed SAPT-based energy decomposition analysis also confirms the diverse and comprehensive nature of the intermolecular binding motifs present in NENCI-2021.

arXiv comment20 pages, 6 figures

References in corpus (19)

• Escaping free-energy minima
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• SchNet - a deep learning architecture for molecules and materials
• van der Waals forces in density functional theory: The vdW-DF method
• By-passing the Kohn-Sham equations with machine learning
• Semistochastic Heat-bath Configuration Interaction method: selected configuration interaction with semistochastic perturbation theory
• ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
• Accurate molecular polarizabilities with coupled-cluster theory and machine learning
• Reliable and Practical Computational Prediction of Molecular Crystal Polymorphs
• Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
• Evidence for supercritical behavior of high-pressure liquid hydrogen
• Fast Semistochastic Heat-Bath Configuration Interaction
• QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
• Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
• Semi-stochastic full configuration interaction quantum Monte Carlo: developments and application
• ACCDB: A Collection of Chemistry DataBases for Broad Computational Purposes
• Gaussian Process-Based Refinement of Dispersion Corrections
• Diagrammatic Coupled Cluster Monte Carlo
• Theory and Implementation of a Novel Stochastic Approach to Coupled Cluster

Cited by in corpus (2)

• S66x8 Noncovalent Interactions Revisited: New Benchmark and Performance of Composite Localized Coupled-Cluster Methods
• A Perspective on Quantum Computing Applications in Quantum Chemistry using 25--100 Logical Qubits