We present a new assessment of the Fermi-Löwdin orbital self-interaction correction (FLO-SIC) approach with an emphasis on its performance for predicting energies as a function of fractional occupation numbers (FONs) for various multielectron systems. Our approach is implemented in the massively parallelized NWChem quantum chemistry software package and has been benchmarked on the prediction of total

The popular method of calculating the noncovalent interaction energies at the coupled-cluster single-, double-, and perturbative triple-excitations [CCSD(T)] theory level in the complete basis set (CBS) limit was to add a CCSD(T) correction term to the CBS second-order Møller-Plesset perturbation theory (MP2). The CCSD(T) correction term is the difference between the CCSD(T) and MP2 interaction energies

Solvation is a notoriously difficult and nagging problem for the rigorous theoretical description of chemistry in the liquid phase. Successes and failures of various approaches ranging from implicit solvation modeling through dielectric continuum embedding and microsolvated quantum chemical modeling to explicit molecular dynamics highlight this situation. Here, we focus on quantum chemical microsolvation

Spin-inversion mechanisms in O2 binding to a model heme complex, consisting of Fe(II)-porphyrin and imidazole, were investigated using density-functional theory calculations. First, we applied the recently proposed mixed-spin Hamiltonian method to locate spin-inversion structures between different total spin multiplicities. Nine spin-inversion structures were successfully optimized for the singlet-triplet

Results of approbation of a new quantum mechanical approach of lone pairs (LPs) visualization, its optimization and testing on a range of model molecules are presented. The main idea of proposed methodology is using 3 He atom as a probe for investigating electronic shells of species with LPs. As model objects, we consider "classical" examples of hydrogen cyanide, methanimine, ammonia, phosphine, formaldehyde

This work demonstrates the potential of machine learning (ML) method to predict catalytic activity of transition metal complex precatalyst toward ethylene polymerization. For this purpose, 294 complexes and 15 molecular descriptors were selected to build the artificial neural network (ANN) model. The catalytic activity can be well predicted by the obtained ANN model, which was further validated by

The convergence of DFT-computed interaction energies with increasing binding site model size was assessed. The data show that while accurate intercalator interaction energies can be derived from binding site models featuring only the flanking nucleotides for uncharged intercalators that bind parallel to the DNA base pairs, errors remain significant even when including distant nucleotides for intercalators

As known, the electron density of covalently bound halogen atoms is anisotropically distributed, making them potentially able to establish many weak interactions, acting at the same time as halogen bond donors and hydrogen bond acceptors. Indeed, there are many examples in which the halogen and hydrogen bond coexist in the same structure and, if a correct bond analysis is required, their separation