Spin state switching on external stimuli is a phenomenon with wide applicability, ranging from molecular electronics to gas activation in nanoporous frameworks. Here, we model the spin crossover as a function of the hydrostatic pressure in octahedrally coordinated transition metal centers by applying a field of effective nuclear forces that compress the molecule towards its centroid. For spin crossover in first-row transition metals coordinated by hydrogen, nitrogen, and carbon monoxide, we find the pressure required for spin transition to be a function of the ligand position in the spectrochemical sequence. While pressures on the order of 1 GPa are required to flip spins in homogeneously ligated octahedral sites, we demonstrate a fivefold decrease in spin transition pressure for the archetypal strong field ligand carbon monoxide in octahedrally coordinated Fe²⁺ in [Fe(II)(NH₃ )₅ CO]²⁺ .

The surprisingly low rupture force and remarkable mechanical anisotropy of rubredoxin have been known for several years. Exploiting the first combination of steered molecular dynamics and the quantum chemical Judgement of Energy DIstribution (JEDI) analysis, the common belief that hydrogen bonds between neighboring amino acid backbones and the sulfur atoms of the central FeS₄ unit in rubredoxin determine the low mechanical resistance of the protein is invalidated. The distribution of strain energy in the central part of rubredoxin is elucidated in real-time with unprecedented detail, giving important insights into the mechanical unfolding pathway of rubredoxin. While structural anisotropy as well as the contribution of angle bendings in the FeS₄ unit have a significant influence on the mechanical properties of rubredoxin, these factors are insufficient to explain the experimentally observed low rupture force. Instead, the rupture mechanism of rubredoxin is far more complex than previously thought and requires more than just a hydrogen bond network.

The computational modeling of molecules under high pressure is a growing research area that augments experimental high-pressure chemistry. Here, a new electronic structure method for modeling atoms and molecules under pressure, Gaussians On Surface Tesserae Simulate HYdrostatic Pressure (GOSTSHYP) approach, is introduced. In this method, a set of Gaussian potentials is distributed evenly on the van der Waals surface of the investigated chemical system, leading to a compression of the electron density and the atomic scaffold. Since no parameters other than pressure need to be specified, GOSTSHYP allows straightforward geometry optimizations and ab initio molecular dynamics simulations of chemical systems under pressure for nonexpert users. Calculated energies, bond lengths, and dipole moments under pressure fall within the range of established computational methods for high-pressure chemistry. A Diels-Alder reaction and the cyclotrimerization of acetylene showcase the ability of GOSTSHYP to model pressure-induced chemical reactions. The connection to mechanochemistry is pointed out.

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 ¹³C, ¹⁵N, and ¹⁷O chemical shifts when compared to ab initio quantum chemistry methods, with the highest accuracy found for ¹H 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 ¹H , as well as indicating that chemical shift prediction for ¹³C, ¹⁵N, and ¹⁷O, which have far fewer training environments than the ¹H atom type, will improve once more unique training samples are made available to exploit the deep network architecture.

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, N₂S₂, shows a positive exaltation despite having aromatic characteristics.

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange-correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear-electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an "open teamware" model and an increasingly modular design.