Using ab initio calculations we have studied the energetics and the evolution of the electronic charge density with shear in three fcc metals exhibiting different deformation properties, aluminum, silver, and iridium. The charge redistribution described by the change in character of specific charge density critical points (cps), is ascertained from the values of the charge density, rho(0), and its three principal curvatures, rho( parallel parallel), rho(hh), and rho(vv), respectively. The change in character of cps correlates with the energetics. For all three metals, rho(hh) vanishes near the unstable stacking configuration. The symmetry or asymmetry of the charge redistribution, measured by rho(hh)/rho(vv), may be an important factor determining stacking fault energies.
In computational thermochemistry, “isodesmic‐type” reactions play a significant role for obtaining accurate thermochemical quantities using low‐cost methods that can be applied to large systems. This review touches on some of the examples. For instance, a series of relative bond dissociation energies (BDEs) have been devised to calculate absolute BDEs with near‐chemical‐accuracy (~5 kJ mol−1) using density functional theory (DFT) methods. To facilitate the applicability of isodesmic‐type reactions, the connectivity‐based hierarchy (CBH) has been developed to automate the systematic generation of isodesmic‐type reaction schemes, and applied to large organic and biomolecular systems. The related netCBH scheme yields accurate reaction energies in complex organic reactions, achieving coupled‐cluster quality results at DFT cost. Isodesmic‐type reactions have been used to obtain heats of formation for medium‐sized fullerenes, with uncertainties of ~20 kJ mol−1 up to C180. In comparison, the literature C60 heat of formation has an uncertainty of 100 kJ mol−1. Importantly, it fills the gap in which heats of formation for those larger fullerenes are not available. These studies showcase how isodesmic‐type reactions propel the accuracy of quantum chemistry computations to a level that rivals or even betters modern experimental determinations, particularly for systems that are difficult to study experimentally. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Electronic Structure Theory > Combined QM/MM Methods Theoretical and Physical Chemistry > Thermochemistry
Recent advances in theoretical thermochemistry have allowed the study of small organic and bio-organic molecules with high accuracy. However, applications to larger molecules are still impeded by the steep scaling problem of highly accurate quantum mechanical (QM) methods, forcing the use of approximate, more cost-effective methods at a greatly reduced accuracy. One of the most successful strategies to mitigate this error is the use of systematic error-cancellation schemes, in which highly accurate QM calculations can be performed on small portions of the molecule to construct corrections to an approximate method. Herein, we build on ideas from fragmentation and error-cancellation to introduce a new family of molecular descriptors for machine learning modeled after the Connectivity-Based Hierarchy (CBH) of generalized isodesmic reaction schemes. The best performing descriptor ML(CBH-2) is constructed from fragments preserving only the immediate connectivity of all heavy (non-H) atoms of a molecule along with overlapping regions of fragments in accordance with the inclusion−exclusion principle. Our proposed approach offers a simple, chemically intuitive grouping of atoms, tuned with an optimal amount of error-cancellation, and outperforms previous structure-based descriptors using a much smaller input vector length. For a wide variety of density functionals, DFT+ΔML(CBH-2) models, trained on a set of small-to medium-sized organic HCNOSClcontaining molecules, achieved an out-of-sample MAE within 0.5 kcal/mol and 2σ (95%) confidence interval of <1.5 kcal/mol compared to accurate G4 reference values at DFT cost.
We introduce a new fragmentation-based molecular representation framework "FragGraph" for QM/ML methods involving embedding fragment-wise fingerprints onto molecular graphs. Our model is specifically designed for delta machine learning (Δ-ML) with the central goal of correcting the deficiencies of approximate methods such as DFT to achieve high accuracy. Our framework is based on a judicious combination of ideas from fragmentation, error cancellation, and a state-of-the-art deep learning architecture. Broadly, we develop a general graph-network framework for molecular machine learning by incorporating the inherent advantages prebuilt into error cancellation methods such as the generalized Connectivity-Based Hierarchy. More specifically, we develop a QM/ML representation through a fragmentationbased attributed graph representation encoded with fragment-wise molecular fingerprints. The utility of our representation is demonstrated through a graph network fingerprint encoder in which a global fingerprint is generated through message passing of local neighborhoods of fragment-wise fingerprints, effectively augmenting standard fingerprints to also include the inbuilt molecular graph structure. On the 130k-GDB9 dataset, our method predicts an out-of-sample mean absolute error significantly lower than 1 kJ/mol compared to target G4(MP2) calculated energies, rivaling current deep learning methods with reduced computational scaling.
Connectivity-Based Hierarchy (CBH) is an effective error-cancellation scheme for the determination of chemically accurate thermochemical properties of a variety of organic and biomolecules. Neutral molecules and open-shell radicals have already been treated successfully by this approach utilizing inexpensive computational methods such as density functional theory. Herein, we present an extension of the method to a new class of molecules, specifically, organic cations. Because of the presence of structural rearrangements involving hydrogen migrations as well as unusual structures such as bridged cations, the application of the standard CBH protocol to a test set of 25 cations leads to significant errors due to ineffective bond-type matching. We propose an adjusted protocol to overcome such limitations to achieve highly effective error cancellation. The modified CBH methods, in conjunction with a wide range of density functionals, reproduce G4 energies for the test set of organic cations accurately within 1-2 kcal/mol at a reduced computational cost.
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