OctaDist, a program for calculating three common octahedral distortion parameters, is presented and the calculation of the trigonal distortion parameter, Θ is standardized for the first time.

Machine learning has become more and more popular in computational chemistry, as well as in the important field of spectroscopy. In this concise review, we walk the reader through a short summary of machine learning algorithms and a comprehensive discussion on the connection between machine learning methods and vibrational spectroscopy, particularly for the case of infrared and Raman spectroscopy. We also briefly discuss state-of-the-art molecular representations which serve as meaningful inputs for machine learning to predict vibrational spectra. In addition, this review provides an overview of the transferability and best practices of machine learning in the prediction of vibrational spectra as well as possible future research directions.

Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of a priori knowledge, enabling one to automatically search for hidden CVs of the reaction of interest.

A series of iron(iii) complexes [Fe(naphEen)2]X·sol (naphEen = 1-{[2-(ethylamino)-ethylimino]methyl}-2-naphtholate; X = F, sol = 0.5CH2Cl2·H2O 1; sol = H2O, X = Cl, 2 and X = Br 3) and [Fe(naphEen)2]I 4 has been prepared. The UV-Vis spectra reveal clear differences for 1 which DFT/TDDFT calculations suggest are due to an equilibrium between [Fe(naphEen)2]F and [Fe(naphEen)2F], the latter having a coordinated F ligand. The X-ray crystal structures of 2-4 show LS Fe(iii) centres in all cases and extensive aryl interactions that link the Fe centres into supramolecular squares. In 3 at room temperature the compound loses half an equivalent of water resulting in a change in space group from Monoclinic P21/n to C2/c. Magnetic studies indicate that 1 is trapped in a mixed spin state being ca. 40% HS while 2-4 are effectively low spin up to 350 K. In contrast, Mössbauer spectroscopic studies of 1 indicate a gradual but incomplete spin crossover. The magnetic properties of 2-4 contrast with the related [Fe(salEen-X)2]anion derivatives which are often spin crossover active.

A simple and rapid functionalization of MOF via microwave-assisted one-pot synthesis afforded a Cu(ii)-Schiff-base-MOF as an efficient catalyst for olefin oxidation.