Automated prediction of ground state spin for transition metal complexes.

Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the cell2mol software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with cell2mol, we construct the TM-GSspin dataset, which contains 2063 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 98% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes.

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.

Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules composed of well-defined building blocks (e.g., peptides) is challenging as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversities. Here, we introduce the OFF-ON (organic fragments from organocatalysts that are non-modular) database, a repository of 7869 equilibrium and 67,457 nonequilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a local kernel regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF-ON data set offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound composed of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.

Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design.

The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generating process.

OSCAR: an extensive repository of chemically and functionally diverse organocatalysts.

The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries, the field of organocatalysis is mostly dominated by case-by-case studies, with a lack of transferable data-driven tools that facilitate both the exploration of a wider range of catalyst space and the optimization of reaction properties. For these reasons, we introduce OSCAR, a repository of 4000 experimentally derived organocatalysts along with their corresponding building blocks and combinatorially enriched structures. We outline the fragment-based approach used for database generation and showcase the chemical diversity, in terms of functions and molecular properties, covered in OSCAR. The structures and corresponding stereoelectronic properties are publicly available (https://archive.materialscloud.org/record/2022.106) and constitute the starting point to build generative and predictive models for organocatalyst performance.

Heteroatom oxidation controls singlet-triplet energy splitting in singlet fission building blocks.

Singlet fission (SF) is a promising multiexciton-generating process. Its demanding energy splitting criterion - that the S1 energy must be at least twice that of T1 - has limited the range of materials capable of SF. We propose heteroatom oxidation as a robust strategy to achieve sufficient S1/T1 splitting, and demonstrate the potential of this approach for intramolecular SF.

Learning the Exciton Properties of Azo-dyes.

The ab initio determination of electronic excited state (ES) properties is the cornerstone of theoretical photochemistry. Yet, traditional ES methods become impractical when applied to fairly large molecules, or when used on thousands of systems. Machine learning (ML) techniques have demonstrated their accuracy at retrieving ES properties of large molecular databases at a reduced computational cost. For these applications, nonlinear algorithms tend to be specialized in targeting individual properties. Learning fundamental quantum objects potentially represents a more efficient, yet complex, alternative as a variety of molecular properties could be extracted through postprocessing. Herein, we report a general framework able to learn three fundamental objects: the hole and particle densities, as well as the transition density. We demonstrate the advantages of targeting those outputs and apply our predictions to obtain properties, including the state character and the exciton topological descriptors, for the two bands (nπ* and ππ*) of 3427 azoheteroarene photoswitches.

Tuning the Thermal Stability and Photoisomerization of Azoheteroarenes through Macrocycle Strain*.

Azobenzene and its derivatives are one of the most widespread molecular scaffolds used in a range of modern applications, as well as in fundamental research. After photoexcitation, azo-based photoswitches revert back to the most stable isomer on a timescale ( t 1 / 2 ) that determines the range of potential applications. Attempts to bring t 1 / 2 to extreme values prompted the development of azobenzene and azoheteroarene derivatives that either rebalance the E- and Z-isomer stabilities, or exploit unconventional thermal isomerization mechanisms. In the former case, one successful strategy has been the creation of macrocycle strain, which tends to impact the E/Z stability asymmetrically, and thus significantly modify t 1 / 2 . On the bright side, bridged derivatives have shown an improved optical switching owing to the higher quantum yields and absence of degradation. However, in most (if not all) cases, bridged derivatives display a reversed thermal stability (more stable Z-isomer), and smaller t 1 / 2 than the acyclic counterparts, which restricts their potential interest to applications requiring a fast forward and backwards switch. In this paper, the impact of alkyl bridges on the thermal stability of phenyl-azoheteroarenes is investigated by using computational methods, and it is revealed that it is indeed possible to combine such improved photoswitching characteristics while preserving the regular thermal stability (more stable E-isomer), and increased t 1 / 2 values under the appropriate connectivity and bridge length.

The Photoisomerization Pathway(s) of Push-Pull Phenylazoheteroarenes*.

Azoheteroarenes are the most recent derivatives targeted to further improve the properties of azo-based photoswitches. Their light-induced mechanism for trans-cis isomerization is assumed to be very similar to that of the parent azobenzene. As such, they inherited the controversy about the dominant isomerization pathway (rotation vs. inversion) depending on the excited state (nπ* vs. ππ*). Although the controversy seems settled in azobenzene, the extent to which the same conclusions apply to the more structurally diverse family of azoheteroarenes is unclear. Here, by means of non-adiabatic molecular dynamics, the photoisomerization mechanism of three prototypical phenyl-azoheteroarenes with increasing push-pull character is unraveled. The evolution of the rotational and inversion conical intersection energies, the preferred pathway, and the associated kinetics upon both nπ* and ππ* excitations can be linked directly with the push-pull substitution effects. Overall, the working conditions of this family of azo-dyes is clarified and a possibility to exploit push-pull substituents to tune their photoisomerization mechanism is identified, with potential impact on their quantum yield.

Thermal spin crossover in Fe(ii) and Fe(iii). Accurate spin state energetics at the solid state.

The thermal spin crossover (SCO) phenomenon refers to an entropy-driven spin transition in some materials based on d6-d9 transition metal complexes. While its molecular origin is well known, intricate SCO behaviours are increasingly common, in which the spin transition occurs concomitantly to e.g. phase transformations, solvent absorption/desorption, or order-disorder processes. The computational modelling of such cases is challenging, as it requires accurate spin state energies in the solid state. Density Functional Theory (DFT) is the best framework, but most DFT functionals are unable to balance the spin state energies. While a few hybrid functionals perform better, they are still too expensive for solid-state minima searches in moderate-size systems. The best alternative is to dress cheap local (LDA) or semi-local (GGA) DFT functionals with a Hubbard-type correction (DFT+U). However, the parametrization of U is not straightforward due to the lack of reference values, and because ab initio parametrization methods perform poorly. Moreover, SCO complexes undergo notable structural changes upon transition, so intra- and inter-molecular interactions might play an important role in stabilizing either spin state. As a consequence, the U parameter depends strongly on the dispersion correction scheme that is used. In this paper, we parametrize U for nine reported SCO compounds (five based on FeII, 1-5 and four based on FeIII, 6-9) when using the D3 and D3-BJ dispersion corrections. We analyze the impact of the dispersion correction treatments on the SCO energetics, structure, and the unit cell dimensions. The average U values are different for each type of metal ion (FeIIvs. FeIII), and dispersion correction scheme (D3 vs. D3-BJ) but they all show excellent transferability, with mean absolute errors (MAE) below chemical accuracy (i.e. MAE <4 kJ mol-1). This enables a better description of SCO processes and, more generally, of spin state energetics, in materials containing FeII and FeIII ions.

Bi-stable spin-crossover in charge-neutral [Fe(R-ptp)2] (ptp = 2-(1H-pyrazol-1-yl)-6-(1H-tetrazol-5-yl)pyridine) complexes.

Bi-stable charge-neutral iron(ii) spin-crossover (SCO) complexes are a class of switchable molecular materials proposed for molecule-based switching and memory applications. In this study, we report on the SCO behavior of a series of iron(ii) complexes composed of rationally designed 2-(1H-pyrazol-1-yl)-6-(1H-tetrazol-5-yl)pyridine (ptp) ligands. The powder forms of [Fe2+(R-ptp-)2]0 complexes tethered with less-bulky substituents-R = H (1), R = CH2OH (2), and R = COOCH3 (3; previously reported)-at the 4-position of the pyridine ring of the ptp skeleton showed abrupt and hysteretic SCO at or above room temperature (RT), whereas complex 5 featuring a bulky pyrene substituent showed incomplete and gradual SCO behavior. The role of intermolecular interactions, lattice solvent, and electronic nature of the chemical substituents (R) in tuning the SCO of the complexes is elucidated.