Abstract:  The average quantum physicist on the street would say that a quantum-mechanical Hamiltonian must be Dirac Hermitian (invariant under combined matrix  transposition and complex conjugation) in order to guarantee that the energy eigenvalues are real and that time evolution is unitary. However, the Hamiltonian $H=p^2+ix^3$, for example, which is obviously not Dirac Hermitian, has a positive real discrete spectrum, generates unitary time evolution, and thus defines a fully consistent and physical quantum theory. Evidently, the axiom of Dirac Hermiticity is too restrictive. While $H=p^2+ix^3$ is not Dirac Hermitian, it is PT symmetric (invariant under combined space reflection P an time reversal T). The quantum mechanics defined by a PT-symmetric Hamiltonian is a complex generalization of ordinary quantum mechanics. When quantum mechanics is extended into the complex domain, new kinds of theories having strange and remarkable properties emerge. Some of these properties have been studied and verified in beautiful laboratory experiments. A particularly interesting PT-symmetric Hamiltonian is $H=p^2-x^4$, which has an upside-down potential. We explain in intuitive and in rigorous terms why the energy levels of this potential are real, positive, and discrete.

Abstract: Reliable theoretical prediction of complex chemical processes in condensed phases requires an accurate quantum mechanical description of interatomic interactions.  If these are to be used in a molecular dynamics calculation, they are often generated “on the fly” from approximate solutions of the electronic Schrödinger equation as the simulation proceeds, a technique known as ab initio molecular dynamics (AIMD).   However, due to the high computational cost of these quantum calculations, alternative approaches employing machine learning methods represent an attractive alternative and have become increasingly popular.  As the adoption of machine-learning potential becomes more widespread, it is important to consider how simulations employing them should be carried out.  Specifically, as they do not implicitly include nuclear quantum effects, these effects must be treated explicitly, for which the most efficient approach involves the use of Feynman path integral techniques.  This is especially important for processes involving light elements.  In this talk, I will discuss how state-of-the-art machine learning potential can be combined with path-integral molecular dynamics to address a variety of challenging chemical problems that have unexpected quantum behavior.  In particular, I will discuss a new class of battery electrolytes that, by harnessing their unusual quantum character, could lead to breakthrough performance.  Discovery of the mechanism of charge transport in these systems could only be achieved by the development of an equivariant transformer network interatomic potential model.  I will also discuss how enhanced sampling techniques can be applied to path integral molecular dynamics to describe the quantum diffusion of hydrogen in structure-II clathrates as a function of temperature, where unexpected inverse quantum effects control diffusion rates at higher temperatures.  Finally, I will discuss an open-chain path integral approach for the grand challenge problem of computing quantum time correlation functions.  The approach leads to an, in principle, exact positive-definite distribution function that can be sampled via Monte Carlo or molecular dynamics to yield exact quantum time correlation functions.  Various approximate and exact numerical schemes for sampling this distribution will be discussed and application to the problem of charge transfer reactions will be presented.

Abstract: Forthcoming

Abstract: Forthcoming

Abstract: The discovery of Earth’s Van Allen radiation belts in 1958 revealed the hazardous radiative environment for spacecraft operating within. Understanding, modeling, and eventually predicting the dynamics of energetic electrons in the radiation belts have long been targets that space physicists have pursued. Since the launch of NASA Van Allen Probes in 2012, significant progress has been achieved in understanding the strong enhancement of relativistic electrons in the radiation belt. However, the fast radiation belt dropout remains unsolved, in which electron fluxes are observed to drop by orders of magnitudes in a few hours. Where do the electrons go during the dropout? This is one of the most important outstanding questions in radiation belt studies. In this talk, I will first briefly review the physical loss processes of energetic electrons that could be responsible for the fast radiation belt dropout. Then I will discuss our recent studies in modeling the loss of energetic electrons during the observed radiation belt dropout, either by transport across the magnetopause into interplanetary space or by precipitation into the atmosphere. Finally, I will discuss the future challenges and opportunities in understanding and modeling the mysterious dropout of radiation belt electrons.

Abstract: The climate crisis is driving a new era of electrification around the globe.  The decarbonization of transportation and industrial processes is expected to make a significant impact on the rate of climate change.  For example, the electrification of refineries and the broader chemical industry has the potential to lead to major reductions in fossil fuel consumption and lower the production of harmful greenhouse gases contributing to climate change.  New components including electrode materials and electrolytes are being discovered quickly and are necessary to engineer functional devices for supporting large scale electrification goals.  However, our understanding of fundamental processes and driving forces that are common across classes of components needed in functional devices is lagging.  In order to predict and tune behaviors of electrolytes or electrodes, for example, we must work to determine behaviors that may apply more broadly across these components.  To gain insights into the nature of electrolytes, electrodes, and the interplay between them, the Krummel Research group uses ultrafast spectroscopy and imaging experiments to characterize components required in devices needed to achieve electrification goals.  In this seminar, I will present snapshots of our recent work regarding electrolytes, electrodes, and an imaging platform we have developed to image chemistry taking place in electrochemical cells.  In each case, I will connect molecular level details available from our ultrafast spectroscopy and imaging experiments to mesoscale behaviors relevant in technologies important for meeting electrification goals to combat climate change.

Abstract: The rare earth elements, hidden at the bottom of the periodic table and long neglected, have risen to prominence at the end of the 20th century. Their unique electronic configuration form the basis for a variety of lasers, photonic applications, strong and exotic magnetism, defining many modern technologies. I will tell a story connecting from the basic science of the geology of Colorado and rare earth and other rare element mineralogy, to our technological and societal dependence and questions of strategic element security. 

Abstract: Forthcoming

Abstract: Forthcoming

Abstract: Forthcoming