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I'm Irina Paci.

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I'm a Professor in the Chemistry department at the University of Victoria.

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My group is interested in doing computer-based simulations,

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modeling of complex materials.

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So a lot of the research in academia and in industry these days

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has to do with designing new materials

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for all kinds of applications 

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for new cell phones,

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larger capacity for storage,

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new computers even.

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But alongside the developments in experimental research,

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there have been a lot of developments in computing.

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We get to use that 

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in order to simulate and model materials

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on longer timescales on a larger scale.

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So we're basically approaching

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the same sort of timescales and length scales

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that experimentalists can approach.

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And in order to do these simulations,

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we need to use very large computers.

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I feel lucky to be a part of a group

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with such diverse research interests

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but we're all tied together by this desire to understand

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materials chemistry at a really fundamental level through simulation.

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One of my favorite recent projects 

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was simulating the catalysts used in hydrogen fuel cells.

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These simulations use quantum mechanics

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to allow us to better understand the properties of good catalysts,

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and they can help identify promising new catalyst candidates

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before making them in a lab.

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The issue though, is that these types of simulations

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are quite expensive to run computationally.

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So running them on a laptop or a normal desktop computer

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is really out of the question.

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In fact

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one of the simulation methodologies that I use

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required a kind of uncommon allocation of computational resources

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and the research computing specialists that manage Arbutus

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were able to configure several compute nodes

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to meet these requirements and ultimately

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help me get the results that I needed for my research.

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I am a computational chemist 

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which means that I use computing clusters 

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to simulate materials at the atomic and molecular scale.

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This is a really important thing to do

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because making these materials in a lab is expensive

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and generates a lot of waste.

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And if we want to make, say, a better semiconductor

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or supercapacitor for charge storage or transportation,

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we need to know what's happening at the atomic scale

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And for this I use 

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the HPC clusters like Cedar or SciNet clusters.

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It's something that I can't do on my laptop

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because these have hundreds or thousands of electrons,

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and the computations are just too large.

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And also, it's quite nice to have somebody else

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to compile my software for me.

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I work on simulating small molecules on surfaces.

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One prevalent issue in device fabrication 

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is the trade-off between efficiency 

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and manufacturing perfect or ideal materials.

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Nanodevices are one of such materials

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and they are used in several purposes, including biosensing.

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Because they are very small

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we can't study them experimentally.

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Hence we use computational methods on supercomputers

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to simulate these devices

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and the reactions that occur on these devices. 

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The research that I've been doing recently

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is related to the adsorption of cysteine on gold surfaces.

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Since cysteine is an amino acid

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it obviously has a lot of applications in Biology,

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and the adsorption itself has relevance in biosensors,

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and perhaps in surface-directed protein synthesis.

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Because of the size of these systems with hundreds of atoms

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and tens of thousands or even hundreds of thousands of electrons,

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we really need high performance computing resources

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which are provided by the DRA.

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Without those resources

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it would just be impossible to do these calculations

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on a personal computer, or even a very powerful one.
