Advancements in the Photocatalysis of Iron Complexes and the Electrocatalysis of Cobalt Complexes for Hydrogen Generation

A family of highly active iron polypyridyl complexes are reported due to their highly active and stable photocatalytic activity. The photocatalytic system produces hydrogen for 24 hours, with a turnover number (TON) of \u3e2000. The photochemical quenching mechanism is explored, as well as the limiting factors of the photocatalytic system. In addition, a family of cobalt Schiff base complexes are reported due to their facile synthesis, cost-effectiveness, and electrocatalytic activity and efficiency. Foot-of-the-wave analysis (FOWA) is used to more ideally describe the kinetics of the electrocatalysts. Tafel plots are used to describe the relationship between activity and efficiency in both the catalysts. One electrocatalyst has a maximum theoretical turnover frequency (TOF) of 80,000 s-1

Diffusion Monte Carlo Using Machine Learning Potential Energy Surfaces

Diffusion Monte Carlo (DMC) is a technique for obtaining the ground-state solution to the vibrational time-independent Schrödinger equation based on a stochastic sampling of an electronic potential energy surface (PES). Ideally, the electronic energies used in DMC are calculated at the CCSD(T) level of theory. Recently, Tom Miller’s group at Caltech has developed a technique for calculating accurate CCSD(T) corrections to the electronic energy at the cost of a standard Hartree-Fock calculation using Gaussian process regression, a machine learning technique\footnote{Welborn, M. , Cheng, L., Miller, T. M. JCTC. 2018 14 (9), 4772-4779}\footnote{Cheng, L., Kovachki, N. B., Welborn, M., Miller, T. M. JCTC. 2019 15 (12), 6668-6677}. DMC is a well-suited technique to validate these PESs, referred to as MOB-ML surfaces, since DMC samples the entire region of the PES in which the vibrational ground state wave function has amplitude. Despite the speed of these MOB-ML PESs relative to an \emph{ab initio} calculation, calculating the Hartree-Fock energy for every one of thousands of DMC configurations over tens of thousands of time-steps makes the use of these surfaces computationally challenging. To this end, we combined two techniques to bring these calculations into computational feasibility. First, we implemented a DMC algorithm to take advantage of massively parallel high performance computing environments. Second, we use this DMC code to collect training data for a neural network (NN). We use a generic algorithm to train the NN to learn the MOB-ML surface based on the points sampled by the DMC. Using TensorFlow, the evaluation of the NN can then easily be done using graphics processing units (GPUs). We have performed small-scale DMC calculations on \chem{H2O} and \chem{CH5+} using these MOB-ML surfaces, and have also run large-scale NN+MOB-ML DMC simulations to obtain a high resolution ground state vibrational wave function and measure of the zero-point energy. Due to the generality of both the MOB-ML surface generation and the NN fitting workflow, we can easily extend this work to larger systems, such as large ion-water clusters or a Criegee intermediate, where running the DMC with traditional electronic structure would be intractable

USING DIFFUSION MONTE CARLO TO GENERATE VIBRATIONAL NORMAL MODES AND SPECTRA: PROTONATED WATER CLUSTERS

Diffusion Monte Carlo (DMC) is a stochastic method used to generate the ground state vibrational wave function of a molecular system. Using DMC, one can understand the effects of vibrational zero-point energy on the structure of interest. Due to the informative physics in the ground state wave function, our group has previously implemented a technique to generate excited state energies and wave functions from DMC in order to understand the vibrational spectroscopy of small water-ion complexes.\footnote{McCoy, A.B; Diken, E. G; Johnson, M.A. JPCA. 2009 113 (26), 7346-7352} \footnote{Timothy L. Guasco, T.L.; Johnson,M.A.; McCoy, A.B. JPCA. 2011 115 (23), 5847-5858} In this work, we extend this approach and apply it to larger protonated water clusters. Specifically, we are investigating the zero-order molecular vibrations and strong vibrational couplings that lead to spectral broadening. To begin, we use DMC to generate the ground state vibrational wave functions of $\mathrm{H^{+}(H_{2}O)_{n=3,4}}$ and $\mathrm{D^{+}(D_{2}O)_{n=3,4}}$. We discover tunneling in a few modes: the umbrella mode of the central hydronium, as well as the rotational coordinate of the outer water molecules. From these observations, we conclude that there are planar, high-symmetry, vibrationally-averaged structures sampled by these ions in their ground states. Using the saddle-point structures to define vibrational coordinates, we build our excited state approximation to investigate which vibrational states contribute intensity to the complicated infrared spectra. In the method’s extension, we refine our approximation of higher order excited states, and we accommodate for strong vibrational coupling through a reduced-dimensional Hamiltonian. The Hamiltonian couples states that are energetically near the fundamental excitation of the hydrogen-bonded OH stretches. The method, in general, allows us to go beyond harmonic approximations by including the anharmonicity of the ground state wave function, yet it also allows us to calculate excited state energies using simple approximations akin to the harmonic oscillator. We find the results of our calculation reinforce the previously held idea that there exist strong couplings between the hydrogen-bonded OH stretch fundamentals and a series of dark modes, which lead to intensity borrowing in both size clusters

Electrocatalytic hydrogen evolution by an iron complex containing a nitro-functionalized polypyridyl ligand

Iron polypyridyl complexes have recently been reported to electrocatalytically reduce protons to hydrogen gas at -1.57 V versus Fc(+)/Fc. A new iron catalyst with a nitro-functionalized polypyridyl ligand has been synthesized and found to be active for proton reduction. Interestingly, catalysis occurs at -1.18 V versus Fc(+)/Fc for the nitro-functionalized complex, resulting in an overpotential of 300 mV. Additionally, the complex is active with a turnover frequency of 550 s(-1). Catalysis is also observed in the presence of water with a 12% enhancement in activity. (C) 2015 Elsevier Ltd. All rights reserved

Development and Analysis of Computational Methods to Study Hydrogen Bonding in Molecular Clusters

Thesis (Ph.D.)--University of Washington, 2022Understanding the role of hydrogen bonding in the structure and dynamics of water is an ongoing challenge in physical chemistry. In particular, understanding how the quantum mechanical effects of molecular vibrations govern the structure and dynamics of water is of interest. The cornerstone method used to study this phenomenon in this work is Diffusion Monte Carlo (DMC), which can be used to obtain the ground state vibrational wave function of any arbitrary molecule or molecular cluster. Instead of attempting to model bulk water and its properties outright, small, gas-phase molecular and ionic clusters of water, which provide model systems to study hydrogen bonding and proton transfer, are studied. To begin, DMC will be reviewed, and PyVibDMC, an open source, general purpose Python DMC software package developed as part of this work, will be discussed. As DMC is rigorously a ground state method, extensions to the DMC approach are required to obtain information about excited states. With excited state information, one can then directly compare simulation to experiment through theoretical and experimental spectroscopy. As such, next, the Ground State Probability Amplitude (GSPA) approximation is presented, and it is applied to protonated water clusters. In the GSPA approach, excited state wave functions are approximated based on simple products of polynomials of vibrational displacements with the ground state DMC wave function. The power of this approach is that one can construct a small basis through which to comprehensively examine the vibrational state space of the chemical system of interest. Extensions to the GSPA approach that incorporate excited state mixing and improved descriptions of higher-order excited states states will be presented as well. These improvements lead to good agreement between the GSPA theoretical and gas-phase experimental vibrational spectra of H7O3+ and H9O4+. Using this rich theoretical approach, we are able to draw connections between the molecular vibrations and structures that govern proton transfer and experimental spectroscopy of the clusters. A methodological procedure is presented next, which is the incorporation of machine learning into the DMC workflow. A potential energy surface is required for DMC simulations. Performing on-the-fly, ab initio potential energy calculations of molecular configurations in DMC simulations for systems beyond a few atoms is computationally intractable. As such, fitted potential energy surfaces are often employed for DMC simulations. However, as systems of interest increase in size, even the evaluations of these fitted surfaces become computationally demanding. To this end, a workflow is developed to use the large amount of data obtained from a small-scale DMC simulation to train a neural network to learn the potential energy surface of interest. Neural network structure, choice of descriptor, and hyperparameter optimization are reviewed and discussed in the context of other machine learning methods, and training data collection strategies are discussed, including the need to sample regions of the potential energy surface that are beyond regions accessed by a typical DMC simulation. Once the neural network surface is trained, it is evaluated in an extremely fast and highly-parallel manner, making DMC simulations significantly more efficient for H2O, CH5+, and (H2O)2. In the final section, DMC is set aside, and an exploration of the correlation between the vibrational spectral signature of an individual water molecule with its surrounding chemical environment is discussed. Specifically, the frequency of a hydrogen-bonded OH stretch in a water dimer pair is correlated to the number of solvating water molecules surrounding it. A quantum mechanical model is constructed to quantify this correlation, and applications of the model to a sample water cluster show the causality between the change in quantum mechanical electron density in the hydrogen bonding region of a particular OH bond and its OH stretch frequency. The application of the quantum model formalizes and explains empirical trends and categorization approaches put forth in previous work to characterize hydrogen bonding environments. This model is then applied to the water network found in a Cs+·(H2O)20 cluster, where these trends are again quantified and then related to both the first and second solvation shell of a hydrogen-bond donor/acceptor water pair within the larger network

Iron Polypyridyl Complexes for Photocatalytic Hydrogen Generation

A series of Fe­(III) complexes were recently reported that are stable and active electrocatalysts for reducing protons into hydrogen gas. Herein, we report the incorporation of these electrocatalysts into a photocatalytic system for hydrogen production. Hydrogen evolution is observed when these catalysts are paired with fluorescein (chromophore) and triethylamine (sacrificial electron source) in a 1:1 ethanol:water mixture. The photocatalytic system is highly active and stable, achieving TONs > 2100 (with respect to catalyst) after 24 h. Catalysis proceeds through a reductive quenching pathway with a quantum yield of over 3%