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Strong correlation has been said to have many faces, and appears to have many synonyms of questionable suitability. In this work we aim not to define the term once and for all, but to highlight one possibility that is both rigorously defined and physically transparent, and remains so in reference to molecules and quantum lattice models. We survey both molecular examples - hydrogen systems (H n , n = 2, 4, 6), Be 2 , H-He-H, and benzene - and the half-filled Hubbard model over a range of correlation regimes. Various quantities are examined including the extent of spin symmetry breaking in correlated single-reference wave functions, energetic ratios inspired by the Hubbard model and the Virial theorem, and metrics derived from the one- and two-electron reduced density matrices (RDMs). The trace and the square norm of the cumulant of the two-electron reduced density matrix capture what may well be defined as strong correlation. Accordingly, strong correlation is understood as a statistical dependence between two electrons, and is distinct from the concepts of "correlation energy" and more general than entanglement quantities that require a partitioning of a quantum system into distinguishable subspaces. This work enables us to build a bridge between a rigorous and quantifiable regime of strong electron correlation and more familiar chemical concepts such as anti-aromaticity in the context of Baird's rule.

Theoretical predictions of NMR chemical shifts from first-principles can greatly facilitate experimental interpretation and structure identification of molecules in gas, solution, and solid-state phases. However, accurate prediction of chemical shifts using the gold-standard coupled cluster with singles, doubles, and perturbative triple excitations [CCSD(T)] method with a complete basis set (CBS) can be prohibitively expensive. By contrast, machine learning (ML) methods offer inexpensive alternatives for chemical shift predictions but are hampered by generalization to molecules outside the original training set. Here, we propose several new ideas in ML of the chemical shift prediction for H, C, N, and O that first introduce a novel feature representation, based on the atomic chemical shielding tensors within a molecular environment using an inexpensive quantum mechanics (QM) method, and train it to predict NMR chemical shieldings of a high-level composite theory that approaches the accuracy of CCSD(T)/CBS. In addition, we train the ML model through a new progressive active learning workflow that reduces the total number of expensive high-level composite calculations required while allowing the model to continuously improve on unseen data. Furthermore, the algorithm provides an error estimation, signaling potential unreliability in predictions if the error is large. Finally, we introduce a novel approach to keep the rotational invariance of the features using tensor environment vectors (TEVs) that yields a ML model with the highest accuracy compared to a similar model using data augmentation. We illustrate the predictive capacity of the resulting inexpensive shift machine learning (iShiftML) models across several benchmarks, including unseen molecules in the NS372 data set, gas-phase experimental chemical shifts for small organic molecules, and much larger and more complex natural products in which we can accurately differentiate between subtle diastereomers based on chemical shift assignments.

Ab initio methods based on the second-order and higher connected moments, or cumulants, of a reference function have seen limited use in the determination of correlation energies of chemical systems over the years. Moment-based methods have remained unattractive relative to more ubiquitous methods, such as perturbation theory and coupled cluster theory, due in part to the intractable cost of assembling moments of high-order and poor performance of low-order expansions. Many of the traditional quantum chemical methodologies can be recast as a selective summation of perturbative contributions to their energy; using this familiar structure as a guide in selecting terms, we develop a scheme to approximate connected moments limited to double excitations. The tractable Doubles Connected Moments [DCM( N )] approximation is developed and tested against a multitude of common single-reference methods to determine its efficacy in the determination of the correlation energy of model systems and small molecules. The DCM( N ) sequence of energies exhibits smooth convergence toward limiting values in the range of N = 11-14, with compute costs that scale as a noniterative O ( M 6 ) with molecule size, M . Numerical tests on correlation energy recovery for 55 small molecules comprising the G1 test set in the cc-pVDZ basis show that DCM( N ) strongly outperforms MP2 and even CCD with a Hartree-Fock reference. When using an approximate Brueckner reference from orbital-optimized (oo) MP2, the resulting oo:DCM( N ) energies converge to values more accurate than CCSD for 49 of 55 molecules. The qualitative success of the method in regions where strong correlation effects begin to dominate, even while maintaining spin purity, suggests this may be a good starting point in the development of methodologies for the description of strongly correlated or spin-contaminated systems while maintaining a tractable single-reference formalism.

We developed and implemented a method-independent, fully numerical, finite difference approach to calculating nuclear magnetic resonance shieldings, using gauge-including atomic orbitals. The resulting capability can be used to explore non-standard methods, given only the energy as a function of finite-applied magnetic fields and nuclear spins. For example, standard second-order Møller-Plesset theory (MP2) has well-known efficacy for 1H and 13C shieldings and known limitations for other nuclei such as 15N and 17O. It is, therefore, interesting to seek methods that offer good accuracy for 15N and 17O shieldings without greatly increased compute costs, as well as exploring whether such methods can further improve 1H and 13C shieldings. Using a small molecule test set of 28 species, we assessed two alternatives: κ regularized MP2 (κ-MP2), which provides energy-dependent damping of large amplitudes, and MP2.X, which includes a variable fraction, X, of third-order correlation (MP3). The aug-cc-pVTZ basis was used, and coupled cluster with singles and doubles and perturbative triples [CCSD(T)] results were taken as reference values. Our κ-MP2 results reveal significant improvements over MP2 for 13C and 15N, with the optimal κ value being element-specific. κ-MP2 with κ = 2 offers a 30% rms error reduction over MP2. For 15N, κ-MP2 with κ = 1.1 provides a 90% error reduction vs MP2 and a 60% error reduction vs CCSD. On the other hand, MP2.X with a scaling factor of 0.6 outperformed CCSD for all heavy nuclei. These results can be understood as providing renormalization of doubles amplitudes to partially account for neglected triple and higher substitutions and offer promising opportunities for future applications., (© 2023 Author(s). Published under an exclusive license by AIP Publishing.)

This paper presents a systematic study of applying composite method approximations with locally dense basis sets (LDBS) to efficiently calculate NMR shielding constants in small and medium-sized molecules. The pcSseg- n series of basis sets are shown to have similar accuracy to the pcS- n series when n ≥ 1 and can slightly reduce computational costs. We identify two different LDBS partition schemes that perform very effectively for density functional calculations. We select a large subset of the recent NS372 database containing 290 H, C, N, and O shielding values evaluated by reference methods on 106 molecules to carefully assess methods of the high, medium, and low computational costs to make practical recommendations. Our assessment covers conventional electronic structure methods (density functional theory and wave function) with global basis calculations, as well as their use in one of the satisfactory LDBS approaches, and a range of composite approaches, also with and without LDBS. Altogether 99 methods are evaluated. On this basis, we recommend different methods to reach three different levels of accuracy and time requirements across the four nuclei considered.

Magnetic properties of molecules such as magnetizabilities represent second order derivatives of the energy with respect to external perturbations. To avoid the need for analytic second derivatives and thereby permit evaluation of the performance of methods where they are not available, a new implementation of quantum chemistry calculations in finite applied magnetic fields is reported. This implementation is employed for a collection of small molecules with the aug-cc-pVTZ basis set to assess orbital optimized (OO) MP2 and a recently proposed regularized variant of OOMP2, called κ -OOMP2. κ -OOMP2 performs significantly better than conventional second order Møller-Plesset (MP2) theory, by reducing MP2's exaggeration of electron correlation effects. As a chemical application, we revisit an old aromaticity criterion called magnetizability exaltation. In lieu of empirical tables or increment systems to generate references, we instead use straight chain molecules with the same formal bond structure as the target cyclic planar conjugated molecules. This procedure is found to be useful for qualitative analysis, yielding exaltations that are typically negative for aromatic species and positive for antiaromatic molecules. One interesting species, N 2 S 2 , shows a positive exaltation despite having aromatic characteristics.

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multiresolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training data set by an order of magnitude without additional cost. We obtain very good agreement for 13 C, 15 N, and 17 O chemical shifts when compared to ab initio quantum chemistry methods, with the highest accuracy found for 1 H chemical shifts that is comparable to the error between the ab initio results and experimental measurements. Principal component analysis (PCA) is used to both understand these greatly improved predictions for 1 H , as well as indicating that chemical shift prediction for 13 C, 15 N, and 17 O, which have far fewer training environments than the 1 H atom type, will improve once more unique training samples are made available to exploit the deep network architecture.

The gas phase reaction between the boron monoxide radical ( 11 BO; X 2 Σ + ) and allene (H 2 CCCH 2 ; X 1 A 1 ) was investigated experimentally under single collision conditions using the crossed molecular beam technique and theoretically exploiting ab initio electronic structure and statistical (RRKM) calculations. The reaction was found to follow indirect (complex forming) scattering dynamics and proceeded via the formation of a van der Waals complex ( 11 BOC 3 H 4 ). This complex isomerized via addition of the boron monoxide radical ( 11 BO; X 2 Σ + ) with the radical center located at the boron atom to the terminal carbon atom of the allene molecule forming a H 2 CCCH 2 11 BO intermediate on the doublet surface. The chemically activated H 2 CCCH 2 11 BO intermediate underwent unimolecular decomposition via atomic hydrogen elimination from the terminal carbon atom holding the boronyl group through a tight exit transition state to synthesize the boronylallene product (H 2 CCCH 11 BO) in a slightly exoergic reaction (55 ± 11 kJ mol -1 ). Statistical (RRKM) calculations suggest that minor reaction channels lead to the products 3-propynyloxoborane (CH 2 ( 11 BO)CCH) and 1-propynyloxoborane (CH 3 CC 11 BO) with fractions of 1.5% and 0.2%, respectively. The title reaction was also compared with the cyano (CN; X 2 Σ + )-allene and boronyl-methylacetylene reactions to probe similarities, but also differences of these isoelectronic systems. Our investigation presents a novel gas phase synthesis and characterization of a hitherto elusive organyloxoborane (RBO) monomer-boronylallene-which is inherently tricky to isolate in the condensed phase except in matrix studies; our work further demonstrates that the crossed molecular beams approach presents a useful tool in investigating the chemistry and synthesis of highly reactive organyloxoboranes.