Artificial neural networks are performing tasks, image recognition and natural language processing, for artificial intelligence. However, these algorithms run on traditional computers and consume orders of magnitude more energy more than the brain does at the same task. One promising path to reduce the energy consumption is to build dedicated hardware to perform artificial intelligence. Nanodevices are particularly interesting because they allow for complex functionality with low energy consumption and small size. I discuss two nanodevices. First, I focus on stochastic magnetic tunnel junctions, which can emulate the spike trains emitted by neurons with a switching rate that can be controlled by an input. junctions can be combined with CMOS circuitry to implement population coding to build low power computing systems capable of controlling output behavior. Second, I turn to different nanodevices, memristors, to implement a different type of computation occurring in nature: swarm intelligence. A broad class of algorithms inspired by the behavior of swarms have been proven successful at solving optimization problems (for example an ant colony can solve a maze). Networks of memristors can perform swarm intelligence and find the shortest paths in mazes, without any supervision or training. These results are striking illustrations of how matching the functionalities of nanodevices with relevant properties of natural systems open the way to low power hardware implementations of difficult computing problems.
We present our recent work on the computational investigations on the charge carrier transport and the excited state decay processes for organic energy materials. We developed a time-dependent vibration correlation function formalism for evaluating the molecular excited state non-radiative decay rate combining non-adiabatic coupling and spin-orbit coupling, to make quantitative prediction for light-emitting quantum efficiency. We proposed a nuclear tunneling enabled hopping model to describe the charge transport in organic semiconductors. An efficient time-dependent DMRG approach is proposed to calculate the optical spectra and carrier dynamics for molecular aggregates.
The Aharonov-Bohm effect (AB) concerns the role in quantum physics of the vector potential of an impenetrable line of magnetic flux. Its partial anticipation by Ehrenberg and Siday, in terms of interference, was an approximation whose wavefunction was not singlevalued, and whose connection with the singlevalued AB wave involves topology: waves winding round the flux (‘many-whirls representation’). AB is a fine illustration of idealization in physics. There are four AB effects, depending on whether the waves and the flux are classical or quantum. In the classical-classical case, many...
We calculate the effect of impurities on the superconducting phase diagram of transition metal dichalcogenide monolayers in the presence of an in-plane magnetic field. Because of strong intrinsic spin-orbit coupling, the upper critical field greatly surpasses the Pauli limit at low temperatures. We find that it is insensitive to intravalley scattering and, ultimately, limited by intervalley scattering.
The microstructure of amorphous alloys attracted many researchers for more than 40 years. Several types of local structures, such as short and/or medium rage ordering have been proposed. Recent computer simulations have made the visible spatial distribution of free volume. However, since all these heterogeneities occur on a very small scale, their effect on the material properties is usually too small to be detected experimentally. However, it can be enhanced by heat treatment under an “external field”. One typical example is the formation of a creep induced magnetic anisotropy when a ferromagnetic amorphous ribbon is annealed under tensile stress. We found by X-ray diffraction and linear thermal expansion measurements (LTE) that this induced magnetic anisotropy originates in local strains frozen-in at room temperature after the annealing stress is released. Thus, a shrinking of the ribbons is observed during post annealing due to the releasing of the frozen-in elastic strain. Figure 1 shows temperature dependence of LTE coefficient, α. All curves show a minimum around the temperature used for the first creep heat treatments. This can be explained by a spatial distribution of the viscosity, η(T). When the alloy is heated to a certain temperature, some regions are still stiff and behave like solid (small η(T)) while the adjacent regions with larger η(T) deform easily. The difference of η(T) is enhanced by the difference in local glass transition temperature. The regions with larger η(T) “glue” the elastic strain in the regions with small η(T) and, hence, freeze it in. The temperature memory effects indicate that the distribution of η(T) does not change during the first annealing. Thus η(T) of the glue regions become large again and these regions start to deform again when the original annealing temperature is reached during post-annealing. Consequently, the elastic strain in the regions with small η(T) is released. The effects of annealing time and the size of heterogeneity will be discussed in this talk.
The incidence rates of cancers and other chronic diseases have been increasing in many regions and populations. There are more than 70,000 new cases of inflammatory bowel diseases (IBD) such as ulcerative colitis, diagnosed every year. Established diagnostic techniques for cancers and ulcerative colitis are invasive, cause discomfort, and are not cost-effective. The compliance rate for the screening of such diseases is very small due to this discomfort, expense, and the risk of complications. Thus, it is important to develop minimally invasive or noninvasive and cost-effective prescreening strategies.
Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy accompanied with different data analysis frameworks can provide an excellent spectroscopic technology to extract biochemical information from bio-fluids which can lead to the identification of diseases. Results show that dried serum samples can be used to detect the biochemical changes induced by cancers and IBDs. This potential technology can be further developed into a noninvasive, personalized diagnostic tool in which patient-to-patient differences in molecular signatures would allow the assessment of disease status and personalized drug management.
We investigate the energetic relaxation and spatial localization of photoexcited states in conformationally disordered p-conjugated models, choosing poly(para-phenylene vinylene) as a model system. Assuming vertical excitations, the initial photoexcited eigenstates are obtained via the disordered Frenkel model. The subsequent relaxation and localization of the excited statesis determined via the disordered Frenkel-Holstein model coupled to a dissipative environment. In particular, we solve the Lindblad master equation via the time-evolving block decimation (TEBD) and quantum jump trajectory methods.
The values of the model parameters physically relevant to polymer systems naturally lead to a separation of time scales, with the ultra-fast dynamics corresponding to energy transfer from the exciton to the internal phonon modes (i.e., the C-C bond oscillations), while the longer time dynamics correspond to damping of these phonon modes by the external dissipation. Associated with these time scales, we investigate the following processes that are indicative of the system relaxing onto the emissive chromophores of the polymer: 1) Exciton-polaron formation occurs on an ultra-fast time scale, with the associated exciton-phonon correlations present within half a vibrational time period of the C-C bond oscillations. 2) Exciton decoherence is driven by the decay in the vibrational overlaps associated with exciton-polaron formation, occurring on the same time scale. 3) Exciton density localization is driven by the external dissipation, arising from ‘wavefunction collapse’ occurring as a result of the system-environment interactions. Finally, we show how fluorescence anisotropy measurements can be used to investigate the exciton decoherence process during the relaxation dynamics.
Quantum transport is a key area in quantum physics which presents challenges in terms of theoretical description. In this talk, I will present how the non-equilibrium dynamics of tunneling junctions weakly coupled to baths of fermionic or bosonic particles can be investigated using open system approaches, namely input-output formalisms and master equations. As a specific example, I will first present our study of electron transport in a quantum dot tunneling junction connecting two normal or superconducting leads, where both single-particle and Cooper-pair tunneling are considered. In particular, I will show how signatures of Andreev bound states can be obtained in the output currents. Then, I will present our results on spin transport in a quadratic spin system connecting baths modeled as XXZ spin chains. Based on non-Markovian master equations for the system and t-Matrix Product States simulations to compute the bath correlation functions, we showed that the spin current through the system can be enhanced due to the presence of the interaction in the baths as well as exhibit transient rectification (i.e. different current under bias exchange). Finally, I will sketch a more general outlook on how non-Markovian master equations could be used to study the transport properties of a system of unknown spectrum, which is particularly useful for the case of complicated time-dependent or many-body system Hamiltonians.
Pittsburgh Supercomputing Center (PSC) offers powerful resources for computing, artificial intelligence, and data management and analytics that are available at no charge for open research and to support coursework. In this talk, we will survey examples of breakthroughs that are using PSC resources and ways to leverage PSC for your own research. Examples will highlight successes in genomics, AI, neuroscience, engineering, and other fields. We will highlight two PSC resources that provide unique capabilities: Bridges and Anton 2. Bridges converges high-performance computing (HPC), artificial intelligence (AI), and Big Data and offers a familiar, an exceptionally flexible user environment, applicable to whatever data analytics or simulation exceed groups’ local capabilities. Anton 2 is a special-purpose computer that dramatically increases the speed of molecular dynamics (MD) simulations to understand the motions and interactions of proteins and other biologically important molecules over much longer time periods than would otherwise be accessible. We will also describe Compass AI, a new initiative to help the community make the most of emerging hardware and software technologies for AI, develop best practices, provide education and training, and establish collaborations, especially between academia and the private sector. We outline areas of expertise at PSC where we are conducting research and open to additional collaboration. We close with a summary of opportunities to co-locate computational resources at PSC, with possible benefits of saving money, bursting to larger resources when needed, and leveraging PSC’s broad software collection.
A number of PSC’s scientific staff will be present for discussion at the reception following the seminar. A reception will follow at 4:30pm
Two dimensional (2D) quantum materials provide a versatile experimental platform to probe spin-dependent novel quantum phenomena emerging at the nanoscale. The possibility of on-demand tuning of spin properties of 2D materials by external knobs such as electric field, substrate engineered proximity, etc., can have far-reaching implications for spintronics. I will discuss our experiments demonstrating a strong modulation of spin currents in bilayer graphene using static and fluctuating proximity exchange fields of a ferromagnetic insulator (FMI). We achieve complete spin modulation in graphene layers by controlling the direction of the exchange field of a nearby magnetic material in graphene/FMI heterostructures. A strong magnetic exchange coupling across the interface in graphene/FMI heterostructures leads to the experimental observation of full spin modulation at low externally applied magnetic fields in mesoscopic graphene spin channels. In graphene/FMI heterostructures, we also discover a novel spin dephasing mechanism due to randomly fluctuating magnetic exchange fields. This is manifested as an unusually strong temperature dependence of the non-local spin signals in graphene, which is due to spin relaxation by thermally-induced transverse fluctuations of the FMI magnetization.
In the second half of my talk, I will discuss spin-charge interconversion driven by the Rashba effect in van der Waal bonded platinum/graphene (Pt/Gr) heterostructures. The interfacial spin-orbit interaction driven Rashba effect in low-dimensional systems can enable efficient and tunable spin-charge interconversion for spintronics applications. I will show that an applied electric field at the Pt/Gr Rashba interface results in a net spin accumulation in graphene, with spin polarization quantized along a direction transverse to the applied electric field. This current induced non-zero spin accumulation at the Pt/Gr interface is a direct consequence of uncompensated spin-textured Femi surfaces of the graphene Dirac states due to a symmetry-breaking electric field normal to the Pt/Gr heterostructure. Employing the Pt/Gr Rashba interface, we also realize the first experimental demonstration of the Onsager reciprocity between charge and spin via Rashba Edelstein effect (REE) and inverse-REE. This work is a significant advancement in graphene spintronics and provides an alternative experimental approach to generating and detecting spins using extrinsically tunable interfacial spin-orbit phenomena in two-dimensional materials.