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The thioether-diphosphine pincer-ligated molybdenum complex, (PSP)MoCl3 (1-Cl3, PSP = 4,5-bis(diisopropylphosphino)-2,7-di-tert-butyl-9,9-dimethyl-9H-thioxanthene) has been synthesized as a catalyst-precursor for N2 reduction catalysis, with a focus on an integrated experimental/computational mechanistic investigation. The (PSP)Mo unit is isoelectronic with the (PNP)Mo (PNP = 2,6-bis(di-t-butylphosphinomethyl)pyridine) fragment found in the family of catalysts for the reduction of N2 to NH3 first reported in 2011 by Nishibayashi and co-workers. Under an atmosphere of N2 the reaction of 1-Cl3 with three reducing equivalents yields the dinuclear penta-dinitrogen Mo complex [(PSP)Mo(N2)2](-N2), 2. Electrochemical studies reveal that 1-Cl3 is significantly more easily reduced than (PNP)MoCl3 (with a potential ca. 0.4 eV less negative). The bridging-nitrogen complex 2 shows no indication of undergoing N2 cleavage to Mo nitride complexes. The reaction of 1-Cl3 with only two reducing equivalents, however, under N2 atmosphere and in the presence of iodide, affords the product of N2 cleavage, the nitride complex (PSP)Mo(N)(I). DFT calculations implicate another N2-bridged complex, [(PSP)Mo(I)]2(N2), as a viable intermediate in facile N2 cleavage to yield (PSP)Mo(N)(I). Conversion of the nitride ligand to NH3 has been studied. If considering sequential addition of H atoms to the nitride, formation of the first N-H bond is by far the thermodynamically least favorable of the three N-H bond formation steps. The first N-H bond was formed by reaction of (PSP)Mo(N)(I) with [LutH]Cl, where coordination of Cl– to Mo plays an essential role. Computations suggest that a second protonation, followed by a rapid and very favorable one-electron reduction, and then a third protonation, furnishes ammonia. In agreement with calculations, ammonia can be generated using either mild H-atom transfer reagents or mild reductants/acids. This comprehensive analysis of the elementary steps of ammonia synthesis and the role of the central pincer donor and halide association provides guidance for future catalyst designs. 

Machine learning approaches have recently been applied to the study of various problems in physics. Most of these studies are focused on interpreting the data generated by conventional numerical methods or the data on an existing experimental database. An interesting question is whether it is possible to use a machine learning approach, in particular a neural network, for solving the many-body problem. In this paper, we present a neural network solver for the single impurity Anderson model, the paradigm of an interacting quantum problem in small clusters. We demonstrate that the neural-network-based solver provides quantitative accurate results for the spectral function as compared to the exact diagonalization method. This opens the possibility of utilizing the neural network approach as an impurity solver for other many-body numerical approaches, such as the dynamical mean field theory. 

The 2-dimensional Ising model on a square lattice is investigated with a variational autoencoder in the non-vanishing field case for the purpose of extracting the crossover region between the ferromagnetic and paramagnetic phases. The encoded latent variable space is found to provide suitable metrics for tracking the order and disorder in the Ising configurations that extends to the extraction of a crossover region in a way that is consistent with expectations. The extracted results achieve an exceptional prediction for the critical point as well as agreement with previously published results on the configurational magnetizations of the model. The performance of this method provides encouragement for the use of machine learning to extract meaningful structural information from complex physical systems where little a priori data is available.