Stability Trends in disubstituted Cobaltocenium Based on the Analysis of the Machine Learning Models.

Cobaltocenium derivatives have shown great potential as components of anion exchange membranes in fuel cells because they exhibit excellent thermal and alkaline stability under operating conditions while allowing for high anion mobility. The properties of the cobaltocenium-anion complexes can be chemically tuned through the substituent groups on the cyclopentadienyl (Cp) rings of the cation CoCp2+. However, the synthesis and characterization of the full range of possible derivatives are very challenging and time-consuming, and while the computational tools can greatly expedite this process, full screening of the electronic structure at a high level of theory is still computationally intensive. Therefore, in this work, we consider the machine learning (ML) modeling as a tool of predicting stability of disubstituted [CoCp2]OH complexes measured by their bond-dissociation energy (BDE). The relevant process here is the dissociation of the cobaltocenium-hydroxide complex into fragments [CoCpY']OH and CpY, where Y and Y' each represent one out of 42 substituent groups of experimental interest. In agreement with the previous ML study of 120 mono- and selected disubstituted species [Wetthasinghe et al. J. Chem. Phys. A (2022) 126], our analysis of the data set expanded to all possible disubstituted cobaltoceniums, points to the highest occupied and lowest unoccupied molecular orbitals, along with the Hirshfeld charge on the singly substituted benzene, to be the key features predicting the BDE of the unseen complexes. Based on the examination of the outliers, the acidity of substituents ((CO)NH2 in our case) is found to be of special significance for the cobaltocenium stability and for the model development. Moreover, we demonstrate that upon the data set refinement, the conventional ML models are capable of predicting the BDE close to 1 kcal/mol based on the properties of just the fragments, thereby greatly reducing the total number of species and of the computational time of each calculation. Such fragment-based "combinatorial" approach to the BDE modeling is noteworthy, since the geometry optimization of complexes in solution is conceptually challenging and computationally demanding, even when leveraging high-performance computing resources.

Breaking the photoswitch speed limit.

The forthcoming generation of materials, including artificial muscles, recyclable and healable systems, photochromic heterogeneous catalysts, or tailorable supercapacitors, relies on the fundamental concept of rapid switching between two or more discrete forms in the solid state. Herein, we report a breakthrough in the "speed limit" of photochromic molecules on the example of sterically-demanding spiropyran derivatives through their integration within solvent-free confined space, allowing for engineering of the photoresponsive moiety environment and tailoring their photoisomerization rates. The presented conceptual approach realized through construction of the spiropyran environment results in ~1000 times switching enhancement even in the solid state compared to its behavior in solution, setting a record in the field of photochromic compounds. Moreover, integration of two distinct photochromic moieties in the same framework provided access to a dynamic range of rates as well as complementary switching in the material's optical profile, uncovering a previously inaccessible pathway for interstate rapid photoisomerization.

Theoretical Examination of the Hydroxide Transport in Cobaltocenium-Containing Polyelectrolytes.

Polymers incorporating cobaltocenium groups have received attention as promising components of anion-exchange membranes (AEMs), exhibiting a good balance of chemical stability and high ionic conductivity. In this work, we analyze the hydroxide diffusion in the presence of cobaltocenium cations in an aqueous environment based on the molecular dynamics of model systems confined in one dimension to mimic the AEM channels. In order to describe the proton hopping mechanism, the forces are obtained from the electronic structure computed at the density-functional tight-binding level. We find that the hydroxide diffusion depends on the channel size, modulation of the electrostatic interactions by the solvation shell, and its rearrangement ability. Hydroxide diffusion proceeds via both the vehicular and structural diffusion mechanisms with the latter playing a larger role at low diffusion coefficients. The highest diffusion coefficient is observed under moderate water densities (around half the density of liquid water) when there are enough water molecules to form the solvation shell, reducing the electrostatic interaction between ions, yet there is enough space for the water rearrangements during the proton hopping. The effects of cobaltocenium separation, orientation, chemical modifications, and the role of nuclear quantum effects are also discussed.

Stability Analysis of Substituted Cobaltocenium [Bis(cyclopentadienyl)cobalt(III)] Employing Chemistry-Informed Neural Networks.

Cationic cobaltocenium derivatives are promising components of the anion exchange membranes because of their excellent thermal and alkaline stability under the operating conditions of a fuel cell. Here, we present an efficient modeling approach to assessing the chemical stability of substituted cobaltocenium CoCp2+, based on the computed electronic structure enhanced by machine learning techniques. Within the aqueous environment, the positive charge of the metal cation is balanced by the hydroxide anion through formation of the CoCp2+OH- complexes, whose dissociation is studied within the implicit solvent employing the density functional theory. The data set of about 118 the CoCp2+OH- complexes based on 42 substituent groups characterized by a range of electron-donating (ED) and electron-withdrawing (EW) properties is constructed and analyzed. Given 12 carefully chosen chemistry-informed descriptors of the complexes and relevant fragments, the stability of the complexes is found to strongly correlate with the energies of the highest occupied and lowest unoccupied molecular orbitals, modulated by a switching function of the Hirshfeld charge. The latter is used as a measure of the electron-withdrawing-donating character of the substituents. On the basis of this observation from the conventional regression analysis, two fully connected, feed-forward neural network (FNN) models with different unit structures, called the chemistry-informed (CINN) and the quadratic (QNN) neural networks, together with a support vector regression (SVR) model are developed. Both deep neural network models predict the bond dissociation energies of the cobaltocenium complexes with mean relative errors less than 10.56% and average absolute errors less than 1.63 kcal/mol, superior to the conventional regression and the SVR model prediction. The results show the potential of QNN to efficiently capture more complex relationships. The concept of incorporating the domain (chemical) knowledge/insight into the neural network structure paves the way to applications of machine learning techniques with small data sets, ultimately leading to better predictive models compared to the classical machine learning method SVR and conventional regression analysis.

Correlation between the Stability of Substituted Cobaltocenium and Molecular Descriptors.

Metallocenium cations, used as a component in an anion exchange membrane of a fuel cell, demonstrate excellent thermal and alkaline stability, which can be improved by the chemical modification of the cyclopentadienyl rings with substituent groups. In this work, the relation between the bond dissociation energy (BDE) of the cobaltocenium (CoCp2+) derivatives, used as a measure of the cation stability, and chemistry-informed descriptors obtained from the electronic structural calculations is established. The analysis of 12 molecular descriptors for 118 derivatives reveals a nonlinear dependence of the BDE on the electron donating-withdrawing character of the substituent groups coupled to the energy of the frontier molecular orbitals. A chemistry-informed feed-forward neural network trained using k-fold cross-validation over the modest data set is able to predict the BDE from the molecular descriptors with the mean absolute error of about 1 kcal/mol. The theoretical analysis suggests some promising modifications of cobaltocenium for experimental research. The results demonstrate that even for modest data sets the incorporation of the chemistry knowledge into the neural network architecture, e.g., through mindful selection and screening of the descriptors and their interactions, paves the way to gain new insight into molecular properties.