The Center for Simulation and Modeling (SaM) at the University of Pittsburgh is dedicated to supporting and facilitating computational-based research across campus. SaM serves as a catalyst for multidisciplinary collaborations among professors, sponsors modeling-focused seminars, teaches graduate-level modeling courses and provides individual consultation in modeling to all researchers at the University. Our areas of research include: energy and sustainability, nanoscience and materials engineering, medicine and biology, and economics and the social sciences.
SaM Researchers in the News
Bio: I received my B.S. in Chemistry from West Virginia University, where I did undergraduate research in the organic synthesis lab of Dr. Xiaodong Shi. Currently, I am working on my Ph.D. at the University of Pittsburgh in the theoretical chemistry lab of Dr. Kenneth Jordan. My areas of study are vibrational spectroscopy of gas-phase molecular clusters and application of quantum chemistry methods to quantum Drude oscillators. Finally, I am starting a project on the correlation-bound superatom anionic states of spherical fullerenes on a Cu(111) surface.
Poster Title: Exploring H3O+ hydration in the gas-phase through infrared spectroscopy
Poster Abstract: The origins of the red-shift in the vibrational signatures of H3O+ in gas-phase H+(H2O)n clusters are presented. Using symmetry-adapted perturbation theory and second-order vibrational perturbation theory, we deduce that the observed red-shift originates from a field-effect due to the first two hydration shells, as well as coupling between the three stretching modes of the H3O+ ion.
Bio: I went to undergrad at Reed College in Portland, Oregon, where I studied physics and liberal arts. After undergrad, I spent three years studying theoretical physics at the University of Washington in Seattle, before leaving to work as a chef. I started my current PhD work in computational biology four years ago, and since then have been working with Dan Zuckerman and a host of collaborators. Our work has focused on the difficulty of understanding complex biological models when stochasticity (randomness) plays an essential role in the process. Using Weighted Ensemble sampling, we are able to drastically reduce the amount of computation needed to study these systems, which makes using detailed models of biological processes much more practical.
More details about Rory’s research are available here.
Poster Title: Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories
Poster Abstract: The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation — by orders of magnitude for some observables.
We review the role that gas-phase, size-selected protonated water clusters, H+(H2O)n, have played in unraveling the microscopic mechanics responsible for the spectroscopic behavior of the excess proton in bulk water. Because the larger (n ≥ 10) assemblies are formed with three-dimensional cage morphologies that more closely mimic the bulk environment, we report the spectra of cryogenically cooled (10 K) clusters over the size range 2 ≤ n ≤ 28, over which the structures evolve from two-dimensional arrangements to cages at around n = 10. The clusters that feature a complete second solvation shell around a surface-embedded hydronium ion yield spectral signatures of the proton defect similar to those observed in dilute acids. The origins of the large observed shifts in the proton vibrational signature upon cluster growth were explored with two types of theoretical analyses. First, we calculate the cubic and semidiagonal quartic force constants and use these in vibrational perturbation theory calculations to establish the couplings responsible for the large anharmonic red shifts. We then investigate how the extended electronic wave functions that are responsible for the shapes of the potential surfaces depend on the nature of the H-bonded networks surrounding the charge defect. These considerations indicate that, in addition to the sizable anharmonic couplings, the position of the OH stretch most associated with the excess proton can be traced to large increases in the electric fields exerted on the embedded hydronium ion upon formation of the first and second solvation shells. The correlation between the underlying local structure and the observed spectral features is quantified using a model based on Badger’s rule as well as via the examination of the electric fields obtained from electronic structure calculations.
Dehydration reactions play an important role to convert biomass-derived alcohols (e.g. ethanol) to value-added chemicals (e.g. ethylene, an important building block for the production of polymers). Dehydration chemistry on metal-oxide catalysts has been an area of research for more than half a century now, albeit, with contradictory results. Prof. Mpourmpakis’ group at Pitt developed a theoretical model based on quantum chemical calculations that relates the dehydration activity with key physicochemical properties of the metal oxides (catalysts) and the alcohols (reactants). These descriptors are the catalyst’s surface Lewis acidity (alcohol binding energy on the metals) and basicity (proton affinity of the surface oxygens or hydroxyl-groups) and the carbenium ion stability of the alcohols. The model’s predictions were further verified by dehydration experiments in Prof. Raymond Gorte’s lab at the University of Pennsylvania. The ramification of this simple, but yet very powerful model is that we can apply it to screen a variety of different alcohols and metal-oxide catalysts according to their dehydration activity, avoiding trial-and-error experiments in the lab.
Publication source: http://pubs.rsc.org/en/content/articlelanding/2014/cy/c4cy00632a