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Herein, we present a new computational methodology that unlocks the prediction of the complex multi-species multi-equilibria processes involved in the formation of complex metal-oxo nanoclusters. Relying on our recently introduced method named POMSimulator, we extended its capabilities and challenged its accuracy with the well-known phosphomolybdate [PMo12O40]3- Keggin anion system. We show how the use of statistical techniques enabled the processing of a vast number of speciation models and their associated systems of non-linear equations efficiently and in a scalable manner. Subsequently, this approach is applied to generate statistically averaged speciation diagrams and their associated error bars. Then, we unveil the previously unreported speciation phase diagram under varying [Mo]/[P] ratios vs. pH. Our findings align well with experimental data, indicating the prevalence of the Keggin {PMo12} as the primary species at low pH, but the lacunary {PMo11}and Strandberg {P2Mo5} anions also emerge as major species at other concentration ratios. Finally, from 7 × 104 speciation models we inferred a plausible reaction network across the diverse nuclearities present within the system, which underlines the role of trimers as key intermediate building blocks.
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Elucidating the speciation (in terms of concentration versus pH) and understanding the formation mechanisms of polyoxometalates remains a significant challenge, both in experimental and computational domains. POMSimulator is a new methodology that tackles this problem from a purely computational perspective. The methodology uses results from quantum mechanics based methods to automatically set up the chemical reaction network, and to build speciation models. As a result, it becomes possible to predict speciation and phase diagrams, as well as to derive new insights into the formation mechanisms of large molecular clusters. In this work we present the main features of the first open-source version of the software. Since the first report [Chem. Sci. 2020, 11, 8448-8456], POMSimulator has undergone several improvements to keep up with the growing challenges that were tackled. After four years of research, we recognize that the source code is sufficiently stable to share a polished and user-friendly version. The Python code, manual, examples, and install instructions can be found at https://github.com/petrusen/pomsimulator.
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Understanding the dynamics of reactive mixtures still challenges both experiments and theory. A relevant example can be found in the chemistry of molecular metal-oxide nanoclusters, also known as polyoxometalates. The high number of species potentially involved, the interconnectivity of the reaction network, and the precise control of the pH and concentrations needed in the synthesis of such species make the theoretical/computational treatment of such processes cumbersome. This work addresses this issue relying on a unique combination of recently developed computational methods that tackle the construction, kinetic simulation, and analysis of complex chemical reaction networks. By using the Bell-Evans-Polanyi approximation for estimating activation energies, and an accurate and robust linear scaling for correcting the computed pKa values, we report herein multi-time-scale kinetic simulations for the self-assembly processes of polyoxotungstates that comprise 22 orders of magnitude, from tens of femtoseconds to months of reaction time. This very large time span was required to reproduce very fast processes such as the acid/base equilibria (at 10-12 s), relatively slow reactions such as the formation of key clusters such as the metatungstate (at 103 s), and the very slow assembly of the decatungstate (at 106 s). Analysis of the kinetic data and of the reaction network topology shed light onto the details of the main reaction mechanisms, which explains the origin of kinetic and thermodynamic control followed by the reaction. Simulations at alkaline pH fully reproduce experimental evidence since clusters do not form under those conditions.
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Heterometallic lanthanide [LnLn'] coordination complexes that are accessible thermodynamically are very scarce because the metals of this series have very similar chemical behaviour. Trinuclear systems of this category have not been reported. A coordination chemistry scaffold has been shown to produce molecules of type [LnLn'Ln] of high purity, i.e. exhibiting high metal distribution ability, based on their differences in ionic radius. Through a detailed analysis of density functional theory (DFT) based calculations, we discern the energy contributions that lead to the unparalleled chemical selectivity of this molecular system. Some of the previously reported examples are compared here with the newly prepared member of this exotic list, [Er2Pr(LA)2(LB)2(py)(H2O)2](NO3) (1) (H2LA and H2LB are two ß-diketone ligands). A magnetic analysis extracted from magnetization and calorimetry determinations identifies the necessary attributes for it to act as an addressable, conditional multiqubit spin-based quantum gate. Complementary ab initio calculations confirm the feasibility of these complexes as composite quantum gates, since they present well-isolated ground states with highly anisotropic and distinct g-tensors. The electronic structure of 1 has also been analyzed by EPR. Pulsed experiments have allowed the establishment of the quantum coherence of the transitions within the relevant spin states, as well as the feasibility of a coherent control of these states via nutation experiments.
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The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entities are interrelated. In this work, we introduce OntoRXN, a novel ontology describing the reaction networks constructed from computational chemistry calculations. Under our paradigm, these networks are handled as undirected graphs, without assuming any traversal direction. From there, we propose a core class structure including reaction steps, network stages, chemical species, and the lower-level entities for the individual computational calculations. These individual calculations are founded on the OntoCompChem ontology and on the ioChem-BD database, where information is parsed and stored in CML format. OntoRXN is introduced through several examples in which knowledge graphs based on the ontology are generated for different chemical systems available on ioChem-BD. Finally, the resulting knowledge graphs are explored through SPARQL queries, illustrating the power of the semantic approach to standardize the analysis of intricate datasets and to simplify the development of complex workflows.
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Interest in the catalytic activation of peroxides, together with the requirement of stereoselectivity for the production of enantiopure sulfoxides, has made sulfoxidation the ideal playground for theoretical and experimental physical organic chemists investigating oxidation reactivity. Efforts have been dedicated for elucidating the catalytic pathway regarding these species and for dissecting out the dominant factors influencing the yield and stereochemistry. In this article, Ti(IV) and Hf(IV) aminotriphenolate complexes have been prepared and investigated as catalysts in the presence of peroxides in sulfide oxidation. Experimental results have been combined with theoretical calculations obtaining detailed mechanistic information on oxygen transfer processes. The study revealed that steric issues are mainly responsible for the formation of intermediates in the oxidation pathway. In particular, we could highlight the occurrence of a blended situation where the steric effects of sulfides, ligands, and oxidants influence the formation of different intermediates and reaction pathways.
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The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library amk-tools has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH3 (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.
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The heterolytic decomposition of tert-butyl peroxyformate to tert-butanol and carbon dioxide, catalyzed by pyridine, is a long-known example of a reaction whose kinetics are strongly affected by solvent polarity. From DFT and ab initio methods together with the SMD implicit solvation model, an extension on the formerly accepted mechanism is proposed. This novel proposal involves the formation of a carbonic acid ester intermediate and its further decomposition, through an unreported pyridine-mediated stepwise route. Computed barriers for this mechanism at DLPNO/CCSD(T)-def2-TZVP are in excellent agreement with experimental kinetic data across different solvents. Furthermore, the strong relationships between activation energies, geometric parameters in the transition state and the characteristics of the different solvents are also analyzed in depth.