Cationic Tetrylene-Iron(0) Complexes: Access Points for Cooperative, Reversible Bond Activation and Open-Shell Iron(-I) Ferrato-Tetrylenes.

The open-shell cationic stannylene-iron(0) complex 4 (4=[PhiP DippSn⋅Fe⋅IPr]+ ; PhiP Dipp={[Ph2 PCH2 Si(i Pr)2 ](Dipp)N}; Dipp=2,6-i Pr2 C6 H3 ; IPr=[(Dipp)NC(H)]2 C:) cooperatively and reversibly cleaves dihydrogen at the Sn-Fe interface under mild conditions (1.5 bar, 298 K), in forming bridging hydrido-complex 6. The One-electron oreduction of the related GeII -Fe0 complex 3 leads to oxidative addition of one C-P linkage of the PhiP Dipp ligand in an intermediary Fe-I complex, leading to FeI phosphide species 7. One-electron reduction reaction of 4 gives access to the iron(-I) ferrato-stannylene, 8, giving evidence for the transient formation of such a species in the reduction of 3. The covalently bound tin(II)-iron(-I) compound 8 has been characterised through EPR spectroscopy, SQUID magnetometry, and supporting computational analysis, which strongly indicate a high localization of electron spin density at Fe-I in this unique d9 -iron complex.

Benchmarking Semiempirical QM Methods for Calculating the Dipole Moment of Organic Molecules.

The dipole moment is a simple descriptor of the charge distribution and polarity and is important for understanding and predicting various molecular properties. Semiempirical (SE) methods offer a cost-effective way to calculate dipole moment that can be used in high-throughput screening applications although the accuracy of the methods is still in question. Therefore, we have evaluated AM1, GFN0-xTB, GFN1-xTB, GFN2-xTB, PM3, PM6, PM7, B97-3c, HF-3c, and PBEh-3c SE methods, which cover a variety of SE approximations, to directly assess the performance of SE methods in predicting molecular dipole moments and their directions using 7211 organic molecules contained in the QM7b database. We find that B97-3c and PBEh-3c perform best against coupled-cluster reference values yielding dipole moments with a mean absolute error (MAE) of 0.10 and 0.11 D, respectively, which is similar to the MAE of density functional theory (DFT) methods (∼0.1 D) reported earlier. Analysis of the atomic composition shows that all SE methods show good performance for hydrocarbons for which the spread of error was within 1 D of the reference data, while the worst performances are for sulfur-containing compounds for which only B97-3c and PBEh-3c show acceptable performance. We also evaluate the effect of SE optimized geometry, instead of the benchmark DFT geometry, that shows a dramatic drop in the performance of AM1 and PM3 for which the range of error tripled. Based on our overall findings, we highlight that there is an optimal compromise between accuracy and computational cost using GFN2-xTB (MAE: 0.25 D) that is 3 orders of magnitude faster than B97-3c and PBEh-3c. Thus, we recommend using GFN2-xTB for cost-efficient calculation of the dipole moment of organic molecules containing C, H, O, and N atoms, whereas, for sulfur-containing organic molecules, we suggest PBEh-3c.

Zeolite encapsulated organometallic complexes as model catalysts.

Heterogeneities in the structure of active centers in metal-containing porous materials are unavoidable and complicate the description of chemical events occurring along reaction coordinates at the atomic level. Metal containing zeolites include sites of varied local coordination and secondary confining environments, requiring careful titration protocols to quantify the predominant active sites. Hybrid organometallic-zeolite catalysts are useful well-defined platform materials for spectroscopic, kinetic, and computational studies of heterogeneous catalysis that avoid the complications of conventional metal-containing porous materials. Such materials have been synthesized and studied previously, but catalytic applications were mostly limited to liquid-phase oxidation and electrochemical reactions. The hydrothermal stability, time-on-stream stability, and utility of these materials in gas-phase oxidation reactions are under-studied. The potential applications for single-site heterogeneous catalysts in fundamental research are abundant and motivate future synthetic, spectroscopic, kinetic, and computational studies.

Trends in computational molecular catalyst design.

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.