Aromaticity in the Spectroscopic Spotlight of Hexaphyrins.

Spectroscopic properties are commonly used in the experimental evaluation of ground- and excited-state aromaticity in expanded porphyrins. Herein, we investigate if the defining photophysical properties still hold for a diverse set of hexaphyrins with varying redox states, topologies, peripheral substitutions, and core-modifications. By combining TD-DFT calculations with several aromaticity descriptors and chemical compound space maps, the intricate interplay between structural planarity, aromaticity, and absorption spectra is elucidated. Our results emphasize that the general assumption that antiaromatic porphyrinoids exhibit significantly attenuated absorption bands as compared to aromatic counterparts does not hold even for the unsubstituted hexaphyrin macrocycles. To connect the spectroscopic properties to the hexaphyrins' aromaticity behaviour, we analyzed chemical compound space maps defined by the various aromaticity indices. The intensity of the Q-band is not well described by the macrocyclic aromaticity. Instead, the degeneracy of the frontier molecular orbitals, the HOMO-LUMO gap, and the |ΔHOMO-ΔLUMO|2 values appear to be better indicators to identify hexaphyrins with enhanced light-absorbing abilities in the near-infrared region. Regions with highly planar hexaphyrin structures, both aromatic and antiaromatic, are characterized by an intense B-band. Hence, we advise using a combination of global and local aromaticity descriptors rooted in different criteria to assess the aromaticity of expanded porphyrins instead of solely relying on the absorption spectra.

Application of Inverse Design Approaches to the Discovery of Nonlinear Optical Switches.

Molecular switches, in which a stimulus induces a large and reversible change in molecular properties, are of significant interest in the domain of photonics. Due to their commutable redox states with distinct nonlinear optical (NLO) properties, hexaphyrins have emerged as a novel platform for multistate switches in nanoelectronics. In this study, we employ an inverse design algorithm to find functionalized 26R→28R redox switches with maximal ßHRS contrast. We focus on the role of core modifications, since a synergistic effect with meso-substitutions was recently found for the 30R-based switch. In contrast to these findings, the inverse design optima and subsequent database analysis of 26R-based switches confirm that core modifications are generally not favored when high NLO contrasts are targeted. Moreover, while push-pull combinations enhance the NLO contrast for both redox switches, they prefer a different arrangement in terms of electron-donating and electron-withdrawing functional groups. Finally, we aim at designing a three-state 26R→28R→ 30R switch with a similar NLO response for both ON states. Even though our best-performing three-state switch follows the design rules of the 30R-based component, our chemical compound space plots show that well-performing three-state switches can be found in regions shared by high-responsive 26R and 30R structures.

Combatting over-specialization bias in growing chemical databases.

BACKGROUND: Predicting in advance the behavior of new chemical compounds can support the design process of new products by directing the research toward the most promising candidates and ruling out others. Such predictive models can be data-driven using Machine Learning or based on researchers' experience and depend on the collection of past results. In either case: models (or researchers) can only make reliable assumptions about compounds that are similar to what they have seen before. Therefore, consequent usage of these predictive models shapes the dataset and causes a continuous specialization shrinking the applicability domain of all trained models on this dataset in the future, and increasingly harming model-based exploration of the space. PROPOSED SOLUTION: In this paper, we propose CANCELS (CounterActiNg Compound spEciaLization biaS), a technique that helps to break the dataset specialization spiral. Aiming for a smooth distribution of the compounds in the dataset, we identify areas in the space that fall short and suggest additional experiments that help bridge the gap. Thereby, we generally improve the dataset quality in an entirely unsupervised manner and create awareness of potential flaws in the data. CANCELS does not aim to cover the entire compound space and hence retains a desirable degree of specialization to a specified research domain. RESULTS: An extensive set of experiments on the use-case of biodegradation pathway prediction not only reveals that the bias spiral can indeed be observed but also that CANCELS produces meaningful results. Additionally, we demonstrate that mitigating the observed bias is crucial as it cannot only intervene with the continuous specialization process, but also significantly improves a predictor's performance while reducing the number of required experiments. Overall, we believe that CANCELS can support researchers in their experimentation process to not only better understand their data and potential flaws, but also to grow the dataset in a sustainable way. All code is available under github.com/KatDost/Cancels .