Structure Optimisation of Large Transition‐Metal Complexes with Extended Tight‐Binding Methods

Paving the road towards automated homogeneous catalyst design

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

ONIOM meets : efficient, accurate, and robust multi-layer simulations across the periodic table

The computational treatment of large molecular structures is of increasing interest in fields of modern chemistry. Accordingly, efficient quantum chemical approaches are needed to perform sophisticated investigations on such systems. This engaged the development of the well-established "Our own N-layered integrated molecular orbital and molecular mechanics" (ONIOM) multi-layer scheme [L. W. Chung et al., Chem. Rev., 2015, 115, 5678-5796]. In this work, we present the specific implementation of the ONIOM scheme into the xtb semi-empirical extended tight-binding program package and its application to challenging transition-metal complexes. The efficient and broadly applicable GFNn-xTB and -FF methods are applied in the ONIOM framework to elucidate reaction energies, geometry optimizations, and explicit solvation effects for metal-organic systems with up to several hundreds of atoms. It is shown that an ONIOM-based combination of density functional theory, semi-empirical, and force-field methods can be used to drastically reduce the computational costs and thus enable the investigation of huge systems at almost no significant loss in accuracy.

Tetramine Aspect Ratio and Flexibility Determine Framework Symmetry for Zn8 L6 Self-Assembled Structures

We derive design principles for the assembly of rectangular tetramines into Zn8 L6 pseudo-cubic coordination cages. Because of the rectangular, as opposed to square, geometry of the ligand panels, and the possibility of either Δ or Λ handedness of each metal center at the eight corners of the pseudo-cube, many different cage diastereomers are possible. Each of the six tetra-aniline subcomponents investigated in this work assembled with zinc(II) and 2-formylpyridine in acetonitrile into a single Zn8 L6 pseudo-cube diastereomer, however. Each product corresponded to one of four diastereomeric configurations, with T, Th , S6 or D3 symmetry. The preferred diastereomer for a given tetra-aniline subcomponent was shown to be dependent on its aspect ratio and conformational flexibility. Analysis of computationally modeled individual faces or whole pseudo-cubes provided insight as to why the observed diastereomers were favored.

Chiral Self-Sorting in Pd6 L12 Metal-Organic Cages

Chiral self-sorting during the formation of cage-like molecules continues to fascinate and advance our understanding of the phenomenon in general. Herein, we report the chiral self-sorting in the Pd₆ L₁₂ -type metal-organic cages. When a racemic mixture of axially chiral bis-pyridyl ligands undergo coordination-driven self-assembly with Pd(II) ions to form Pd₆ L₁₂ -type cages, the system has the option of chiral self-sorting to afford any of at least 70 pairs of (one homochiral and 69 heterochiral) enantiomers and 5 meso isomers or a statistical mixture of everything. However, the system resulted in diastereoselective self-assembly through a high-fidelity chiral social self-sorting to form a racemic mixture of D₃ symmetric heterochiral [Pd₆ (L^6R/6S )₁₂ ]¹²⁺ /[Pd₆ (L^6S/6R )₁₂ ]¹²⁺ cages.

Automated and Efficient Generation of General Molecular Aggregate Structures

Modeling intermolecular interactions of complex non-covalent structures is important in many areas of chemistry. To facilitate the generation of reasonable dimer, oligomer, and general aggregate geometries, we introduce an automated computational interaction site screening (aISS) workflow. This easy-to-use tool combines a genetic algorithm employing the intermolecular force-field xTB-IFF for initial search steps with the general force-field GFN-FF and the semi-empirical GFN2-xTB method for geometry optimizations. Compared with the alternative CREST program, aISS yields similar results but with computer time savings of 1-3 orders of magnitude. This allows for the treatment of systems with thousands of atoms composed of elements up to radon, e.g., metal-organic complexes, or even polyhedra and zeolite cut-outs which were not accessible before. Moreover, aISS can identify reactive sites and provides options like site-directed (user-guided) screening.

Synthesis of [60]Fullerene Hybrids Endowed with Steroids and Monosaccharides: Theoretical Underpinning as Promising anti-SARS-CoV-2 Agents

Cyclopropanation reactions between C60 and different malonates decorated with monosaccharides and steroids using the Bingel-Hirsch methodology have allowed the obtention of a new family of hybrid compounds in good yields. A complete set of instrumental techniques has allowed us to fully characterize the hybrid derivatives and to determine the chemical structure of monocycloadducts. Besides, the proposed structures were investigated by cyclic voltammetry, which evidenced the exclusive reductive pattern of fullerene Bingel-type monoadducts. Theoretical calculations at the DFT-D3(BJ)/PBE 6-311G(d,p) level of the synthesized conjugates predict the most stable conformation and determine the factors that control the hybrid molecules' geometry. Some parameters such as polarity, lipophilicity, polar surface area, hydrophilicity index, and solvent-accessible surface area were also estimated, predicting its potential permeability and capability as cell membrane penetrators. Additionally, a molecular docking simulation has been carried out using the main protease of SARS-CoV-2 (Mpro) as the receptor, thus paving the way to study the potential application of these hybrids in biomedicine.

In Silico Optimization of Charge Separating Dyes for Solar Energy Conversion

Dye-sensitized photoelectrochemical cells are promising devices in solar energy conversion. However, several limitations still have to be addressed, such as the major loss pathway through charge recombination at the dye-semiconductor interface. Charge separating dyes constructed as push-pull systems can increase the spatial separation of electron and hole, decreasing the recombination rate. Here, a family of dyes, consisting of polyphenylamine donors, fluorene bridges, and perylene monoimide acceptors, was investigated in silico using a combination of semi-empirical nuclear dynamics and a quantum propagation of photoexcited electron and hole. To optimize the charge separation, several molecular design strategies were investigated, including modifying the donor molecule, increasing the π-bridge length, and decoupling the molecular components through steric effects. The combination of a triphenylamine donor, using an extended 2-fluorene π-bridge, and decoupling the different components by steric hindrance from side groups resulted in a dye with significantly improved charge separation properties in comparison to the original supramolecular complex.

Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems

Active space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial but a nontrivial task. In this article, we present a neural network-based approach for automatic selection of active spaces, focused on transition metal systems. The training set has been formed from artificial systems composed of one transition metal and various ligands, on which we have performed the density matrix renormalization group and calculated the single-site entropy. On the selected set of systems, ranging from small benchmark molecules up to larger challenging systems involving two metallic centers, we demonstrate that our machine learning models could predict the active space orbitals with reasonable accuracy. We also tested the transferability on out-of-the-model systems, including bimetallic complexes and complexes with ligands, which were not involved in the training set. Also, we tested the correctness of the automatically selected active spaces on a Fe(II)-porphyrin model, where we studied the lowest states at the DMRG level and compared the energy difference between spin states or the energy difference between conformations of ferrocene with recent studies.

Assessing Density Functional Theory for Chemically Relevant Open-Shell Transition Metal Reactions

Due to the principle lack of systematic improvement possibilities of density functional theory, careful assessment of the performance of density functional approximations (DFAs) on well-designed benchmark sets, for example, for reaction energies and barrier heights, is crucial. While main-group chemistry is well covered by several available sets, benchmark data for transition metal chemistry is sparse. This is especially the case for larger, chemically relevant molecules. Addressing this issue, we recently introduced the MOR41 benchmark which covers chemically relevant reactions of closed-shell complexes. In this work, we extend these efforts to single-reference open-shell systems and introduce the "reactions of open-shell single-reference transition metal complexes" (ROST61) benchmark set. ROST61 includes accurate coupled-cluster reference values for 61 reaction energies with a mean reaction energy of -42.8 kcal mol^-1. Complexes with 13-93 atoms covering 20 d-block elements are included, but due to the restriction to single-reference open-shell systems, important elements such as iron or platinum could not be taken into account, or only to a small extent. We assess the performance of 31 DFAs in combination with three London dispersion (LD) correction schemes. Further, DFT-based composite methods, MP2, and a few semiempirical quantum chemical methods are evaluated. Consistent with the results for the MOR41 closed-shell benchmark, we find that the ordering of DFAs according to Jacob's ladder is preserved and that adding an LD correction is crucial, clearly improving almost all tested methods. The recently introduced r²SCAN-3c composite method stands out with a remarkable mean absolute deviation (MAD) of only 2.9 kcal mol^-1, which is surpassed only by hybrid DFAs with low amounts of Fock exchange (e.g., 2.3 kcal mol^-1 for TPSS0-D4/def2-QZVPP) and double-hybrid (DH) DFAs but at a significantly higher computational cost. The lowest MAD of only 1.6 kcal mol^-1 is obtained with the DH DFA PWPB95-D4 in the def2-QZVPP basis set approaching the estimated accuracy of the reference method. Overall, the ROST61 set adds important reference data to a sparsely sampled but practically relevant area of chemistry. At this point, it provides valuable orientation for the application and development of new DFAs and electronic structure methods in general.

High-Throughput Computational Evaluation of Low Symmetry Pd2 L4 Cages to Aid in System Design*

Unsymmetrical ditopic ligands can self-assemble into reduced-symmetry Pd2 L4 metallo-cages with anisotropic cavities, with implications for high specificity and affinity guest-binding. Mixtures of cage isomers can form, however, resulting in undesirable system heterogeneity. It is paramount to be able to design components that preferentially form a single isomer. Previous data suggested that computational methods could predict with reasonable accuracy whether unsymmetrical ligands would preferentially self-assemble into single cage isomers under constraints of geometrical mismatch. We successfully apply a collaborative computational and experimental workflow to mitigate costly trial-and-error synthetic approaches. Our rapid computational workflow constructs unsymmetrical ligands and their Pd2 L4 cage isomers, ranking the likelihood for exclusively forming cis-Pd2 L4 assemblies. From this narrowed search space, we successfully synthesised four new, low-symmetry, cis-Pd2 L4 cages.

Efficient Computation of Geometries for Gold Complexes

Computationally obtaining structural parameters along a reaction coordinate is commonly performed with Kohn‐Sham density functional theory which generally provides a good balance between speed and accuracy. However, CPU times still range from inconvenient to prohibitive, depending on the size of the system under study. Herein, the tight binding GFN2‐xTB method [C. Bannwarth, S. Ehlert, S. Grimme, J. Chem. Theory Comput. 2019, 15, 1652] is investigated as an alternative to produce reasonable geometries along a reaction path, that is, reactant, product and transition state structures for a series of transformations involving gold complexes. A small mean error (1 kcal/mol) was found, with respect to an efficient composite hybrid‐GGA exchange‐correlation functional (PBEh‐3c) paired with a double‐ζ basis set, which is 2–3 orders of magnitude slower. The outlined protocol may serve as a rapid tool to probe the viability of proposed mechanistic pathways in the field of gold catalysis.

Robust Atomistic Modeling of Materials, Organometallic, and Biochemical Systems

Modern chemistry seems to be unlimited in molecular size and elemental composition. Metal‐organic frameworks or biological macromolecules involve complex architectures and a large variety of elements. Yet, a general and broadly applicable theoretical method to describe the structures and interactions of molecules beyond the 1000‐atom size regime semi‐quantitatively is not self‐evident. For this purpose, a generic force field named GFN‐FF is presented, which is completely newly developed to enable fast structure optimizations and molecular‐dynamics simulations for basically any chemical structure consisting of elements up to radon. The freely available computer program requires only starting coordinates and elemental composition as input from which, fully automatically, all potential‐energy terms are constructed. GFN‐FF outperforms other force fields in terms of generality and accuracy, approaching the performance of much more elaborate quantum‐mechanical methods in many cases.