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April 2018
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  1. Center for Functional Nanomaterials Seminar

    11 am, CFN, Bldg. 735 2nd floor seminar room

    Hosted by: Deyu Lu

    Google has recently revealed its 72-qubit quantum computer, named Bristlecone. Earlier, Intel released a 49-qubit chip called Tangle Lake, and IBM had built a quantum computer with 50 qubits. D-Wave also boasts about their quantum annealing systems with more than 2000 qubits. What's all the fuss about? Quantum computation is a novel way of information processing that allows, for certain classes of problems, exponential speedup over classical computation. Various models of quantum computation exist, such as the adiabatic, circuit, and measurement-based models, but operate very differently and may suit different physical realizations. I will give a pedagogical introduction to quantum computation. I will give you an idea why quantum computers seem powerful and yet it is not easy to design quantum algorithms that outperform classical ones. Such quantum algorithms do exist. But quantum computers can be used to simulate other quantum systems. I will also mention several recent examples of experiments on quantum simulations. This potentially opens up many potential applications of quantum computers, in addition to other quantum algorithmic pursuits. I will also discuss the idea of quantum error correction in order for the quantum computer to retain its coherence and resist errors. One of the characteristic trait of quantum mechanics is entanglement, and during the execution of a quantum algorithm, entanglement is generated. Entanglement itself can also enable tasks that are otherwise impossible. If time permits I will briefly discuss my own research on how to exploit entanglement as a resource for quantum computation. This talk does not assume much background and should be accessible to physicists, mathematician, chemists, computer scientists and engineers.

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  1. Center for Functional Nanomaterials Seminar

    1:30 pm, CFN, Bldg. 735, Conference Room A, 1st Floor

    Hosted by: Mircea Cotlet

    My research group exploits fluorescence imaging, in particular single molecule imaging, to study chemical and biological processes at the molecular (or nano-) and cellular levels with unprecedented spatiotemporal resolution and sensitivity. In this presentation I will discuss our recent findings towards studying DNA-based nanomaterials. Starting from fundamental photophysical and photochemical studies towards achieving fluorophore photostability.1 I will next describe how improvements on fluorophore photostability have paved the way to single molecule studies on the assembly, structure, morphology and robustness of DNA nanotubes.2 Emphasis will be placed on the enormous opportunities that single molecule imaging provides to interrogate and study supramolecular materials at the molecular level.

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  1. APR

    20

    Friday

    Center for Functional Nanomaterials Seminar

    11 am, CFN, Bldg. 735, Conference Room A, 1st Floor

    Friday, April 20, 2018, 11:00 am

    Hosted by: Oleg Gang

    Molecular simulations are becoming indispensable tools for designing and characterizing soft matter systems, such as proteins, nucleic acids, polymers, and surfactants. In this talk, I will present a combined simulation-experimental approach toward optimized DNA biosensor design. We developed a coarse-grained model for simulating DNA hybridization on surface. Simulations performed using this model reveals presence of conformational heterogeneities corresponding to partially hybridized structures on the surface, which results in false positives and false negatives during sequence detection. Presence of such conformational heterogeneities was later confirmed with wet lab experiments. We propose a solution of this problem by customizing the sensor surface area according to the molecular dimension, which will increase the diagnostic accuracy of hybridization-based DNA sequence detection methods. In the final part of the talk, I will discuss how advanced machine learning techniques can be used to tackle the emerging "big data" problem in molecular simulation research. With recent advances of computing power, emerging efficient sampling schemes, and wide interest in soft matter simulations, the cumulative amount of simulation data is becoming massive. How to merge, analyze, and build predictive models from this massive amount of data thus becomes a research challenge. I will demonstrate how machine learning techniques enable better understanding of large-scale, heterogeneous protein simulation data.

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  1. APR

    27

    Friday

    Center for Functional Nanomaterials Seminar

    11 am, Bldg 735, CFN, Seminar Room 2nd Floor

    Friday, April 27, 2018, 11:00 am

    Hosted by: Huolin Xin

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  1. APR

    20

    Friday

    Center for Functional Nanomaterials Seminar

    "Characterizing The Soft Matter Landscape Using Molecular Simulations and Machine Learning"

    Presented by Payel Das, IBM Thomas J. Watson Research Center

    11 am, CFN, Bldg. 735, Conference Room A, 1st Floor

    Friday, April 20, 2018, 11:00 am

    Hosted by: Oleg Gang

    Molecular simulations are becoming indispensable tools for designing and characterizing soft matter systems, such as proteins, nucleic acids, polymers, and surfactants. In this talk, I will present a combined simulation-experimental approach toward optimized DNA biosensor design. We developed a coarse-grained model for simulating DNA hybridization on surface. Simulations performed using this model reveals presence of conformational heterogeneities corresponding to partially hybridized structures on the surface, which results in false positives and false negatives during sequence detection. Presence of such conformational heterogeneities was later confirmed with wet lab experiments. We propose a solution of this problem by customizing the sensor surface area according to the molecular dimension, which will increase the diagnostic accuracy of hybridization-based DNA sequence detection methods. In the final part of the talk, I will discuss how advanced machine learning techniques can be used to tackle the emerging "big data" problem in molecular simulation research. With recent advances of computing power, emerging efficient sampling schemes, and wide interest in soft matter simulations, the cumulative amount of simulation data is becoming massive. How to merge, analyze, and build predictive models from this massive amount of data thus becomes a research challenge. I will demonstrate how machine learning techniques enable better understanding of large-scale, heterogeneous protein simulation data.

  2. APR

    27

    Friday

    Center for Functional Nanomaterials Seminar

    "Electrocatalysis: From nanoelectrochemistry to materials design"

    Presented by Professor Dr. Wolfgang Schuhmann, Ruhr-Universität Bochum, Analytical Chemistry and Center for Electrochemical Sciences (CES), Germany

    11 am, Bldg 735, CFN, Seminar Room 2nd Floor

    Friday, April 27, 2018, 11:00 am

    Hosted by: Huolin Xin