Methyllithium (MeLi) is the parent archetypical organolithium complex and its monomeric form is vital for understanding the ubiquitous organolithium-mediated reactions. However, despite being pursued for decades, to the best of...

The ability to electrochemically store energy is crucial for the integration of intermittent renewable energy sources such as wind and solar power into modern energy grids. Among a wide variety of possible solutions, redox flow batteries (RFBs) are especially attractive as their energy content and power output can be scaled independently, offering a high degree of flexibility. For RFBs, polyoxometalates (POMs) are very appealing as these transition metal oxide nanoclusters exhibit the ability to store multiple electrons in a reversible manner. However, despite the interest in their properties, the link between the POM structure and its redox properties remains unclear. In this contribution, we study the redox potentials of [SiW 12 O 40 ] 4− (SiW 12 ) and [PV 14 O 42 ] 9− (PV 14 ) using a number of different theoretical methods. We first adopt the thermodynamic cycle approach combined with quantum chemistry and implicit solvation to estimate the redox potentials. Subsequently, we use molecular dynamics to facilitate an explicit description of the solvent environment. The implicit solvation model is semiquantitative, and problems arise when strong solute−solvent interactions are present. Using two approaches, thermodynamic integration and fractional number of electrons methods, we show that explicitly including the solvent environment can improve the calculated redox potentials for strong solute−solvent interactions and also gives important atomistic insights into its nature and how it changes upon reduction. Our results illustrate the performance of these approaches for addressing the challenging problem of simulating the redox potentials in POMs and provides the framework to develop a more detailed understanding of the structure−property relationships that exist.

No abstract

The ongoing integration of quantum chemistry, statistical mechanics, and artificial intelligence is paving the route toward more effective and accurate strategies for the investigation of the spectroscopic properties of medium-to-large size chromophores in condensed phases. In this context we are developing a novel workflow aimed at improving the generality, reliability, and ease of use of the available computational tools. In this paper we report our latest developments with specific reference to unsupervised atomistic simulations employing non periodic boundary conditions (NPBC) followed by clustering of the trajectories employing optimized feature spaces. Next accurate variational computations are performed for a representative point of each cluster, whereas intracluster fluctuations are taken into account by a cheap yet reliable perturbative approach. A number of methodological improvements have been introduced including, e.g., more realistic reaction field effects at the outer boundary of the simulation sphere, automatic definition of the feature space by continuous perception of solute–solvent interactions, full account of polarization and charge transfer in the first solvation shell, and inclusion of vibronic contributions. After its validation, this new approach has been applied to the challenging case of solvatochromic effects on the UV–vis spectra of a prototypical nitroxide radical (TEMPO) in different solvents. The reliability, effectiveness, and robustness of the new platform is demonstrated by the remarkable agreement with experiment of the results obtained through an unsupervised approach characterized by a strongly reduced computational cost as compared to that of conventional quantum mechanics and molecular mechanics models without any accuracy reduction.