THE ANNUAL 2015 DEIXIS DEPARTMENT OF ENERGY COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP SCIENCE GRADUATE OF ENERGY COMPUTATIONAL DEPARTMENT
BRIGHT IDEA
Eric Isaacs probes how metal nanoparticles supercharge sunlight’s water-splitting feat PAGE 5 THE ANNUAL DEIXIS TABLE OF CONTENTS
DEIXIS, The DOE CSGF Annual is published by the Krell Institute. Krell administers the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) program for the DOE under contract DE-FG02-97ER25308.
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Copyright 2015 Krell Institute. All rights reserved. 4 22 Practicum Profiles Winning CYSE Essay Summer Application Communicate Your Science DEIXIS (ΔΕΙΞΙΣ — pronounced da¯ksis) transliterated from classical Greek into the Roman alphabet, means 5 Eric Isaacs || Shining a Light on & Engineering Contest a display, mode or process of proof; the process of Water-splitting Reactions 22 Andrew Stershic || Building Batteries showing, proving or demonstrating. DEIXIS can also refer to the workings of an individual’s keen 9 Aurora Pribram-Jones || Putting Theory from the Microstructure Up intellect, or to the means by which such individuals, into Practice, Under Pressure e.g. DOE CSGF fellows, are identified. 13 Aaron Sisto || Conducting Ensembles DEIXIS is an annual publication of the Department of to Ferret Out Features 24 Energy Computational Science Graduate Fellowship Howes Scholar program that highlights the work of fellows and alumni. Matthews Recognized for Leadership DOE CSGF funding is provided by the DOE Office of 16 in Computational Chemistry Advanced Scientific Computing Research (ASCR) within Alumni Profiles the Office of Science and the Advanced Simulation and Computing (ASC) program within the National Nuclear From Alumni to Leaders Security Administration. 16 Matthew Norman || Helping Users Get 26 Science From Titan Class of 2015 18 Amoolya Singh || Creating Tiny Factories 20 John Dolbow || Extending a Method for Collaboration 28 Editor Creative Project Fellows Directory Shelly Olsan Coordinator Buffy Clatt Senior Science Writer Design Thomas R. O’Donnell julsdesign, inc.
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SUMMER APPLICATION FELLOWS USE AND LEARN SKILLS ON PRACTICUMS
The Department of SHINING A LIGHT ON Energy Computational WATER-SPLITTING REACTIONS
Science Graduate Fellowship supports the THE PRACTICUM is a highlight of the Department of Energy Computational ERIC ISAACS Science Graduate Fellowship (DOE CSGF). For 12 weeks, fellows set aside their doctoral nation’s brightest science Columbia University research and work alongside scientists at DOE national laboratories. Brookhaven National Laboratory and engineering students, As the word denotes, the emphasis is on practice: Fellows apply what they’ve learned to allowing them to problems of national importance. concentrate on learning AS A YOUTH, ERIC ISAACS MOVED from the Midwest to the For example, Eric Isaacs left Columbia University for Brookhaven National Laboratory and research. The work West Coast. He went from there to the East Coast for his doctoral studies. But he traveled on New York’s Long Island, where he helped decipher a photocatalytic effect that splits less than 70 miles for his 2013 practicum. water molecules. The project was only tangential to his doctoral inquiries into potential new of more than 325 DOE Isaacs, a Department of Energy Computational Science Graduate Fellowship (DOE battery materials. CSGF) recipient, studies applied physics at Columbia University in New York. His CSGF alumni has helped practicum was just a couple hours away (in light traffic), at Long Island’s Brookhaven At the University of California, Irvine, Aurora Pribram-Jones usually relied on models to refine the United States remain National Laboratory. methods to calculate materials’ electronic structures. At Sandia National Laboratories in For Isaacs, an intellectual bond was more important than physical proximity. “I was New Mexico, however, she tackled real-world shock physics computations. competitive in a interested in forming connections to researchers at Brookhaven and in knowing what’s global economy. going on there,” he says, since it’s packed with experts and high-performance computers. At Stanford University, Aaron Sisto researched the fundamental details behind As a youth, Isaacs lived in a Cleveland suburb, but attended high school near light-harvesting bacteria. But at Lawrence Livermore National Laboratory in California, he ~~~~~ ALos Angeles after his father, a surgeon, relocated the family. At the University of California, learned tools to mathematically identify the most influential properties for making three- Berkeley, Isaacs first majored in chemistry, but was frustrated by how little his introductory dimensionally printed metal parts. courses discussed chemical principles’ underlying mechanisms. He switched to physics because “it seemed to be the most fundamental way of looking at nature – going down to All three fellows can attest to the impact of the practicum. the lowest level.” This first-principles, or ab initio, approach is key to Isaacs’ doctoral research under Chris Marianetti, associate professor of materials science and applied physics and applied mathematics. They develop quantum mechanical models to track how electrons behave in complicated materials, hoping to find compounds that make batteries hold more electricity while absorbing and releasing it efficiently. As the word denotes, the emphasis is on practice: Fellows apply what “We’re using computer simulations to predict, rather than measure directly, properties of materials, particularly properties relevant to actual things you’d want to do with these they’ve learned to problems of national importance. Image courtesy of Shangmin Xiong materials,” such as store energy, Isaacs says. ~~~~~ Isaacs’ summer project with computational scientist Yan Li was more about explaining materials’ properties than predicting them. It arose from experiments at Stony Brook University in New York State, where researchers study cadmium sulfide, a semiconductor. When exposed to sunlight, it acts as a weak photocatalyst for hydrogen production from
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‘We’re really focused on why it works and STRETCHING TO A what’s going on at the atomic level.’ SURPRISING RESEARCH RESULT ~~~~~
water. The team found sprinkling a slab of the Vienna Ab initio Simulation Package, Computations ran at the National The project Eric Isaacs took as he joined Chris the material with nanoparticles, each made a popular electronic structure code, to Energy Research Scientific Computing Marianetti’s materials science and engineering group of a few dozen gold atoms at most, increases model these configurations and compute Center at Lawrence Berkeley National proved more interesting than a mere exercise to learn hydrogen generation by as much as 35 their properties. Laboratory and on a cluster at Brookhaven’s new techniques. times. They also found platinum, a common “A crystal has different facets,” Li Center for Functional Nanomaterials. The subject was monolayer materials: sheets between catalyst, had a similar effect. says, and researchers must know which Isaacs is a coauthor on a paper, one and a few atoms thick that are remarkably strong and The process could help make hydrogen facet is at the surface before simulating it. published in the Journal of Physical have unusual properties. In a previous study, Marianetti’s a clean, plentiful energy source, but it’s Experimental data gave few clues as to the Chemistry C, reporting the simulations group found that graphene, a single layer of carbon atoms, puzzling. “Gold is an inert noble metal. You correct one “so we had to try ourselves.” of platinum nanoparticles on a nonpolar underwent a surprising transition as it broke under stress. think it’s not going to be that chemically Using the Python language, Isaacs surface. The first author, Stony Brook He suggested Isaacs see what happens to other monolayers active,” Isaacs says. Exactly why it and wrote a script to cut the simulated cadmium graduate student Shangmin Xiong, did under similar conditions. Using Brookhaven National Laboratory supercomputers, platinum (another noble metal) supercharge sulfide surface along a crystal orientation. much of the work, Li says, but Isaacs “really Isaacs modeled graphene, boron nitride, molybdenum the reaction is “a big scientific question.” “I didn’t teach him anything,” Li says. helped to kick-start the project.” He knew disulfide and graphane (graphene in which each carbon Li, now an editor for the journal Isaacs studied a software tutorial, then the theory behind the models and already atom bonds with a hydrogen atom). Many such monolayers Physical Review B, and her team wanted to “generated a surface, all by himself. That was familiar with the computer codes to are built of six-atom backbones linked like a hexagon. The model interactions between the cadmium was very impressive.” run them. “It only took a couple days to let simulations stretched the sheets equally in all directions. sulfide and the nanoparticles. They wanted Isaacs investigated how rearranging him start doing some calculations,” Li says. In monolayer materials, as in all substances, atoms to calculate how atoms at the interface atoms near the surface affects the substrate “This was definitely a happy surprise.” rapidly vibrate in place. In most cases, stretching leads to transfer negative or positive charges properties. He researched surfaces with Results indicate that nanoparticle size elastic instability, in which atomic bonds continuously break and how electron energy levels in the either polar (having nonzero dipole greatly influences the water-splitting and the material separates uniformly. nanoparticles align with those in moment – separated positive and negative effect, Li says, but it’s unclear which works Isaacs’ models indicate that monolayer materials’ the substrate. charges) or nonpolar orientations and best with the surface to promote the vibrational modes change under stress. “There’s a subtle “Part of the reason we were studying probed their electronic properties. For reaction. Simulations can help unravel instability that happens” instead of elastic instability, he says. “The material will not just start vibrating in that mode this is for fundamental reasons: to know polar cadmium sulfide surfaces, Isaacs this, but they’ll require more detailed – and return to equilibrium, but will keep going,” essentially how we can use these catalysts and examined cases in which the surface and demanding – approaches, she adds. transforming into a new structure. In this “soft mode” the nanotechnology to improve this reaction,” terminated with cadmium atoms versus Isaacs presented a poster on the molecules tend to separate into isolated hexagons. Isaacs says. “We’re really focused on sulfur atoms. practicum work at a Brookhaven Young In an elastic instability model, the new structure will why it works and what’s going on at the The polar surface configuration the Researcher Symposium in November break by that point. “In these calculations, we’re not seeing atomic level.” researchers tried in an early simulation 2013. Marianetti says the experience the actual breaking process, but we’re predicting when it To get there, Li uses density produced answers that matched poorly “widened (Isaacs’) view by pushing him would happen and what the mechanism is.” It turns out this functional theory (DFT), an ab initio with research data. Before the practicum into an area of problems that he wouldn’t change from the beginning vibrational mode into another Projections from the top and side of distorted structures for technique that accounts for quantum ended, they deduced that a nonpolar have seen in my research group.” unstable structure limits the materials’ strength. (a) graphene, (b) boron nitride, (c) graphane and (d) molybdenum mechanical conditions in which electrons molecular orientation works better. This instability was unexpected, but it was more disulfide each strained equally in all directions. The carbon, behave as both particles and waves. Because it’s arduous to exactly MAKING A TRANSITION surprising that all the materials were susceptible despite boron, nitrogen, hydrogen, molybdenum and sulfur atoms are calculate interactions at the cadmium That perspective could help as their different electronic properties. Isaacs “generalized this represented as brown, green, silver, white, purple and yellow AGAINST THE GRAIN sulfide surface, the team also needed a Isaacs investigates materials containing for most of the monolayer materials,” Marianetti says. spheres, respectively. Dashed lines indicate the undistorted First the models needed to describe simplified representation of the noble transition metals: elements with partially “What he showed, which was not obvious, is that most of strained lattice. In graphene, boron nitride and graphane the how atoms are arranged in the crystalline metal nanoparticles. Isaacs researched filled electron shells, allowing them to them shared a very similar, if not identical, instability. It was backbone distorts toward isolated six-atom rings, while beautiful work.” molybdenum disulfide undergoes a distinct distortion toward cadmium sulfide surface. Different the scientific literature to devise one. easily receive and donate electrons – a The results could help researchers better predict the trigonal pyramidal coordination. Reprinted with permission from arrangements lead to different surface “It’s pretty challenging, if you’re not an key battery cathode material property. strain a material can take and find ways to delay or E.B. Isaacs and C.A. Marianetti, Phys. Rev. B 89, 184111 (2014). qualities, like structure and charge experimentalist, to make some educated Most transition metal compounds overcome this soft mode to strengthen substances. polarity, that affect interactions with the guess on the best model,” he adds. “We in cathodes are oxides, with the metal nanoparticles. It’s a bit like how wood worked with our experimentalist friends coupled to oxygen atoms. Many already cut against the grain differs from wood to help out, but there was a lot of digging.” are in devices like cellphones. But cut with the grain. Isaacs and Li used numerous transition metal oxides are
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PUTTING THEORY INTO PRACTICE, UNDER PRESSURE
AURORA PRIBRAM-JONES University of California, Irvine Sandia National Laboratories, New Mexico
AURORA PRIBRAM-JONES DEALS in the intangible in her theoretical chemistry doctoral research at the University of California, Irvine. She tinkers with algorithms’ innards, using desktop computers to test new methods on model problems. “Big science is very different from what I get to experience every day,” she says. ‘Big science is very Pribram-Jones got a regular dose of big science on her 2013 practicum at Sandia National Laboratories in New Mexico. The Department of Energy Computational Science different from what Graduate Fellowship (DOE CSGF) recipient used high-performance computing (HPC) I get to experience systems to examine real materials. She worked with engineers and physicists who design Electron density difference isosurfaces after the adsorption of a 19-atom platinum cluster (left) and a 38-atom and run high energy density physics experiments. Many of the tests take place on the Z platinum cluster (right) on a cadmium sulfide surface. Red and blue denote electron gain and loss, respectively. every day.’ Pulsed Power Facility, or Z machine, a warehouse-sized device that delivers tremendous Image courtesy of Shangmin Xiong, from “Adsorption characteristics and size/shape dependence of Pt clusters energy to tiny targets. The pulses subject materials to unearthly pressures and temperatures. on CdS surface,” S. Xiong, E. Isaacs and Y. Li, J. Phys. Chem. C, 2015, 119 (9), PP 4834-4842. ~~~~~ APribram-Jones was based nearby. “It’s so cool,” she exclaims. “You know it’s running every day, because you get the shudder in your office when they send it down line.” The practicum expanded Pribram-Jones’ research skills, which focus on refining techniques to understand matter’s fundamental properties under extreme conditions. Her work can edge us toward greater knowledge of the universe and potential new strongly correlated materials – “notoriously Isaacs studies. DFT, Marianetti says, this to battery materials, which would be energy sources. difficult to understand” substances, Isaacs “claims you should be able to diffuse sort of a massive leap.” What Pribram-Jones learned in her journey to graduate school, however, is as important says. “To go beyond the current technology lithium in and out and it should mix” as It’s a perfect subject, Isaacs adds: as her classroom education. Her talents would have mattered little without determination and even just to better understand how it the battery charges. Experiments show just “We’re looking at questions of basic science, and help from understanding educators. works, we need to improve our descriptions the opposite: It separates into phases of but we’re also looking at a technologically Pribram-Jones grew up in East Palo Alto, California, next door to Palo Alto, the home of strongly correlated materials and to iron phosphate and lithium iron phosphate, relevant material in hopes this can make of Stanford University. Both her parents fought drug addiction and her father’s health predict properties of new ones.” limiting how fast the battery can charge an impact.” failed, putting him out of work as a fire protection system installer. After that, “we got pretty DFT maps the many-body problem, and discharge. In his off hours, Isaacs sometimes Credit: Sandia National Laboratories poor pretty quickly,” she says. Her mother, a teacher, struggled to hold the family in which every electron interacts with To understand this and other strongly makes a different kind of impact: punching (including Pribram-Jones’ younger brother) together while fighting her own addiction and every other electron, onto an easier, correlated materials, Marianetti’s group an opponent in the boxing ring. “It’s good mental illness. non-interacting electron problem. But combines DFT with dynamical mean field exercise,” he says, and “very helpful to reset Despite their problems, “my parents were really amazing people,” Pribram-Jones says. in strongly correlated materials, some theory (DMFT). It calculates electron after so much of reading papers and coding.” Books were plentiful and her mother, a neuroscientist’s daughter, raised her children to electrons interact intimately. “Because interactions more explicitly, but can’t cope Isaacs aims to knock out his doctorate think scientifically, even when making sandwiches. If someone put the peanut butter lid on of that, it’s very difficult, or in some with a huge number. Instead, it calculates sometime in 2016. Regardless of where he the counter face down, her mother would warn, “You’re going to contaminate your sample.” sense impossible in practice, to model the strongly correlated interactions while works, he wants to connect his research Pribram-Jones never completed high school, but passed proficiency tests that allowed material at a level that considers just single DFT accounts for the rest. with everyday products that improve life. her to enroll as a music major at Foothill College, a community college. At 15, Pribram- electrons,” as DFT does, Isaacs says. Marianetti says his group has worked After all, “the battery in the cellphone Jones moved to Southern California for a year in a search for stability. Her father died a year Take, for example, lithium iron on combining DMFT and DFT, but “Eric I’m using right now was only an idea in a after she returned to East Palo Alto. She tutored students in bassoon, math and reading and phosphate, a promising battery material is pushing forward toward actually applying lab not long ago.” worked other jobs to help support her family.
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research requires in a way few students can. deep inside planets, especially gas giants starting at ambient temperature and This snapshot from a molecular dynamics “There’s only a dozen or so people in the like Saturn. Inertial confinement fusion pressure, with a powerful shock. Researchers simulation shows the hot disordered world who can really think that way and she (ICF) experiments, like those at the can add points to the graph by performing state of aluminum as the material releases will become one of them if she keeps at it.” Department of Energy’s National Ignition successively stronger shock experiments. toward ambient pressure following a Burke and Pribram-Jones focus on Facility, also create WDM in tiny capsules With each one, Desjarlais says, “I get a new high-pressure shock, with an added density functional theory (DFT), a of frozen hydrogen. end state (in the material) that is a slight isosurface (three-dimensional representation) technique to calculate electron interactions “It’s either very expensive or impossible increase in density and increase in pressure.” of electron charge density. It shows some in molecules. It’s an ab initio method: to do experiments on matter in those Plotting those points yields the Hugoniot. of the electrons localized near ions while others fill a continuum between the Preset conditions or experimental data conditions,” Burke says, so mathematical “It’s a way of encapsulating all the different atoms. Aurora Pribram-Jones’s thesis don’t inform the simulations. The method methods to predict WDM properties responses of that ambient material to a research has helped develop a deeper applies fundamental rules from the strange are critical. variety of shock strengths.” understanding of the precise electron world of quantum mechanics, in which The release isentrope comes after the density distribution under these electrons can be both particles and waves. UNDER PRESSURE shock passes. With nothing to confine the warm dense matter conditions. Image DFT is accurate but consumes less WDM also is found in shocked material, it expands isentropically – without courtesy of Michael Desjarlais, Sandia computer resources than other quantum materials – like those tested in the Z heat entering or leaving. “The material National Laboratories. chemistry techniques, making it computational machine. Sandia Senior Scientist Michael doesn’t go back to its initial state when you chemists’ go-to method. It’s used on complex Desjarlais, Pribram-Jones’ practicum release it, because when you shocked it you problems in energy, physics and chemistry. supervisor, builds ab initio models that dramatically increased its entropy,” or Rather than apply DFT to real often emulate or predict Z experiments. thermodynamic disorder, Desjarlais says. materials, however, Pribram-Jones probes Desjarlais and his colleagues use DFT and For example, the material could be shocked its mechanisms. “I work on things that other methods to calculate what happens into a plasma state that dissipates into the elucidate how the actual theory itself works,” when materials like diamond are subjected surrounding environment. The release A few years later, Pribram-Jones was the cause – ruptured discs in her neck Pribram-Jones chose theoretical using “math tools and physics tools to look to extreme conditions, as in a terrific shock. isentrope is different for each Hugoniot point. managing a Palo Alto bookstore. She often had impinged on a nerve – but also chemistry for graduate school because it at fundamental problems so we can use The results go into complex codes In the past, researchers used theoretical stayed after closing and fell asleep while discovered a benign tumor in her connects two favorite subjects – chemistry insights from those to crack open more simulating the interiors of giant planets, models to produce equation of state tables reading books from the technical section. collarbone. Pribram-Jones took a year off and math – but also because she was unsure complicated problems.” Instead of studying ICF experiments, and other phenomena. from which scientists could extract Hugoniot When she returned to Foothill in 2003, to recover and deal with a mountain of whether she could handle theory. “I tend to real materials, they’re “like thought At Sandia, Pribram-Jones first learned points and release isentropes, Desjarlais Pribram-Jones had learned enough to test medical bills – but still sat in on classes. choose the path that I find scariest,” she experiments. They allow us to start peeling to use the Vienna Ab initio Simulation says. But for “high-fidelity work, like the out of chemistry and math classes. She “I had worked very hard to get there, says, because she learns new things. the onion and get to more interesting, Program (VASP), a code combining DFT work we do on the Z machine, those tables earned simultaneous associate’s degrees so I felt I had really failed,” she says. Her difficult circumstances have given fundamental pieces of information.” with molecular dynamics, a classical physics are often not accurate enough.” For in chemistry, math and biology. Understanding professors gave her jobs Pribram-Jones an unusual intellectual The phenomenon Pribram-Jones technique. She used it to compute two key improvement, researchers turn to ab in a chemical stock room and assisting maturity, says Kieron Burke, her doctoral wants to help others understand with her properties for aluminum: the high-pressure initio DFT calculations like the ones A NEW OBSTACLE with labs, and she returned to graduate in advisor. “I think it comes from being DFT ideas is warm dense matter (WDM), Hugoniot and the release isentrope. Pribram-Jones did on her practicum. Pribram-Jones transferred to Harvey chemistry. “It was definitely very hard and exposed to many different environments a high-temperature, high-density state The Hugoniot is like a line on a graph. “The Z machine is an amazing, amazing Mudd College, a small California school I’m lucky I had advocates on the faculty.” and dealing with all sorts of situations. between plasma and solid but with properties To find each point, researchers strike a experimental tool,” she says, but it’s known for its emphasis on engineering, That experience pushes her to help others … What comes up in academia is child’s of both. WDM is found in explosions and material (aluminum in this case), usually impossible to test materials under all the science and math, and did well in her first as a teacher and mentor, a mission she play relative to some of the things” year. But one morning she awoke to find plans to continue after earning her Pribram-Jones has faced. What’s more, he her left arm paralyzed. Doctors found doctorate this year. says she grasps the logical abstraction his
‘I tend to choose the path that I find scariest.’ ~~~~~
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conditions important to science. It’s crucial accurate for most purposes, Desjarlais CONDUCTING ENSEMBLES “to have calculations we can predict will says, giving researchers the confidence TO FERRET OUT FEATURES work well at a certain temperature or to use it more freely. pressure range.” Since leaving Sandia, Pribram-Jones Researchers use aluminum as a has continued collaborating with Desjarlais benchmark material to help deduce the to calculate the aluminum Hugoniot and AARON SISTO properties of other materials in a shock isentrope values at even more extreme Stanford University experiment. Pribram-Jones’ work will pressures and temperatures. She’s also Lawrence Livermore National Laboratory contribute data to a new standard for contributing to a paper on the research. aluminum, Desjarlais says. The practicum plunged Pribram-Jones Another part of Pribram-Jones’ project deep into high-performance computing aimed to cut the computer time needed to for the first time. Using Red Sky, Sandia’s WHEN HE STARTED his 2013 Lawrence Livermore National Laboratory calculate release isentropes. She compared Sun Microsystems supercomputer, was “like (LLNL) practicum, Aaron Sisto just wanted to learn data mining techniques. the accuracy of a method Desjarlais developed learning to drive in a Ferrari,” she says. “It Yet the project he completed may also help advance additive manufacturing – with another that’s simpler to compute but was very, very different than the model three-dimensional printing – and has led him to found his own company. thermodynamically approximate. calculations I do.” Sisto, a son of Chicago artists, is driven to find real-world applications for what he Pribram-Jones ran VASP simulations Burke says the experience made learns and discovers. He studied mechanical engineering at Purdue University because it using both methods and analyzed the Pribram-Jones better at intuitively focused on the practical aspects of design and creation. Later, Sisto says, he became results. Both techniques calculate isentrope recognizing when an algorithm’s results More than 130 factors affect interested in peripheral subjects that required a perspective outside his field. He switched points and use a Mathematica tool to are illogical. The big benefit, however, was to computational chemistry for his doctoral studies at Stanford University and earned a the final product’s quality. determine where to do the next step, but working with experts in vastly different Department of Energy Computational Science Graduate Fellowship (DOE CSGF). the simpler technique uses fewer intermediate subjects from her usual area. “You don’t Sisto’s quest for new perspectives also pushed him to work with Chandrika Kamath of steps, Pribram-Jones says. She determined get that kind of thing without doing ~~~~~ WLLNL’s Center for Applied Scientific Computing and learn techniques for finding the key the simpler method was sufficiently something like a practicum.” nuggets in mounds of information. He studied feature selection algorithms, mathematical methods to identify the most influential variables in a data set. Kamath, an experienced data-mining researcher, and her colleagues provided Sisto a good test for his new skills: selective laser melting (SLM), in which a powerful beam fuses metal powder particles, layer by layer, into a solid object. It’s a versatile but complex 3-D printing technology: More than 130 factors affect the final product’s quality. The LLNL team wanted to identify the combination of SLM parameters needed to Lasers are used to align diagnostics and create stainless steel parts with greater than 99 percent density. They could have printed hardware prior to shooting on Sandia’s dozens of tests, each with a different blend of parameter settings, to find the best mix, but Z machine. Computer models predict that would consume huge amounts of time and money. They needed to limit the parameter and emulate experiments on the giant space to only the most influential factors. pulsed-power device. Credit: Sandia Credit: Lawrence Livermore National Laboratory Kamath’s role was to use simple simulations and experiments to quickly find the key National Laboratories. process parameters for high-density parts. Other LLNL scientists had run a model to calculate the length, width and depth of the melt pool – the puddle formed when the laser hits the bed of powder particles. Each simulation combined four parameters: power, scan speed, beam size, and how much energy the material absorbs. Centering the simulations on just four parameters still left a significant data problem, as each can have a range of settings, like 10 different scan speeds and seven power levels. Kamath wanted to answer the question of whether any of the four parameters had a greater effect on density than others. That connected to Sisto’s feature-selection work. “It just so happened we were working on this additive manufacturing problem and there were data from some of the simulations,” Kamath says. “So it seemed like one could apply those techniques (Sisto) was implementing to this data set.”
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‘You can turn light into any form of energy you want and direct it in any way that’s useful.’ Sisto and Martinez concentrate on now found in many of the world’s fastest As a result, Martinez says, the method light-harvesting complex II, a structure machines. The algorithm uses a parallel can calculate excitation dynamics in ~~~~~ found in photosynthetic bacteria. It’s hierarchy: At the top level, individual molecules of at least 4,000 atoms. That comprised of chromophores – molecules chromophores are sent to computer could make it a candidate to run on an that capture light energy. The problem, nodes comprised of GPUs and CPUs for exascale computer capable of a million Martinez says, is “excitation transfers from simultaneous calculation. Building on trillion (1018) calculations per second, chromophore to chromophore, and each Martinez’s earlier GPU research, processors about a thousand times faster than today’s Sisto first investigated feature-selection unimportant,” he says, much as candidates different techniques and appreciate the issues chromophore is typically between 50 and in the hybrid nodes also work in parallel to best machines. algorithms, which identify the most polling below a certain level can be in applying them to real data. From that point 200 atoms.” compute the chromophore’s complex By helping scientists understand light influential parameters or rank them by regarded as having no impact on an election. of view, it was a successful practicum.” Modeling even a few dozen atoms electronic interactions at the lowest level. absorption, Sisto says, his algorithms may importance. There are numerous such The results indicated that scan speed Sisto’s doctoral advisor, Todd Martinez, from first principles requires powerful “Then we patch the system back one day lead them to tune the process in methods, Kamath says, but the biases in and laser power affected melt-pool width says the experience sharpened Sisto’s ideas computers. Martinez says Sisto leapt the together in an extremely efficient way that useful ways. “That will open up a range of each can suggest varying paths, similar and depth the most, whereas power and about using computers, with machine- hurdle through a combination of physical gives you back all the information about possibilities, because you can turn light to how opinion poll results can diverge. the material’s ability to absorb that power learning and data-mining tools, to design insight and computing prowess. The insight: this massive complex,” Sisto says. into any form of energy you want and most affected length. Other factors generally molecules. “That’s really what drives him,” Although electron interactions are tightly Joining chromophores to calculate a direct it in any way that’s useful.” CONDUCTING THE ENSEMBLE ranked below the noise baseline. he adds. coupled within each chromophore, coupling light-harvesting system’s total energy isn’t a For now, Sisto is considering how his “When you use any of these techniques, With that information, Kamath and Those ideas also pushed Sisto to start between chromophores is weak. “Rather new idea, Martinez says, but it usually was methods can be applied more generally to you have to be very careful because the her fellow researchers geared experiments QComponents, a company offering rapid than having all the electrons talking to each used to fit parameters so the model results make better decisions and reach milestones outcome might be dependent on what data toward power and scan speed, using design and prototyping of new molecular other at once, you can solve the problem in matched observed phenomena. Sisto more efficiently – one of his goals for set you’re working with,” she says. “You SLM to build one 10-mm square, 7-mm electronics. Such activities often are pieces,” Martinez says, tracking interactions instead uses the chromophore model as QComponents. never really apply these techniques blindly.” tall stainless steel pillar for each of 48 time-consuming and expensive for within a chromophore but “only talking an organizing framework for first-principles With “molecular design just on the cusp Instead, researchers often use an ensemble combinations of the two. The best high-tech firms, he says. “The idea was very weakly to the remainder of the system.” calculations, without fitting data. “The of being relevant to industry,” Martinez says, of methods and average their conclusions densities resulted when the laser power to use skills I have in high-performance Sisto says the algorithm runs on real genius of all this,” Martinez says, is to the company may be how Sisto distinguishes to improve accuracy, like how averaging was highest (400 watts), even when speed computing and in simulation and data hybrid high-performance computers – take “ideas that were used as models where himself. “He could make an impact there.” opinion polls can predict an election more varied from 1,900 mm per second to 2,200 mining and put those into an integrative ones using both standard processors you would fit data, and turn that into a accurately than any individual poll. mm per second, said an International framework” to help companies. (CPUs) and graphics processing units coarse-graining principle” for parceling Sisto used the C++ programming Journal of Advanced Manufacturing At Stanford, Sisto builds detailed (GPUs), video game chip descendants out work. language to build an ensemble of five Technology paper Sisto coauthored computer models of excitation energy feature-selection algorithms. Each with Kamath and Livermore scientists transfer: Electrons are boosted to higher evaluated the relative importance of Bassem El-dasher, Gilbert Gallegos energies by light or other sources and the four factors used in the laser and Wayne King. then interact with the environment to simulations – power, scan speed, beam The results were consistent with the transport that electronic energy across size and absorptivity – and rated each ensemble’s predictions, Sisto says. “This multiple molecules. This visualization shows for its effect on the melt pool. Each analysis directed the search in two of these the similarity matrix that feature-selection algorithm ranked the parameters. If there had been hundreds A LIGHT CROP describes the relative parameters on a five-point scale from (of parameters), this would never have It’s key to light harvesting: how distances between individual least important to most important. been possible.” organisms convert photons into chemical points in a data set used to test an ensemble feature- Still, Sisto says, ranking “doesn’t Sisto worked to give the package, energy. What Sisto and Martinez, the selection algorithm. Each necessarily tell you that the highest rank named redDRC (reduced dimensionality, David Mulvane Ehrsam and Edward point in the plot represents is the most important and the lowest is regression and classification), a usable Curtis Franklin professor in chemistry the similarity between completely unimportant. All that tells you interface and to ensure it could handle and professor of photon science, learn two data points, with is relative to each other, that’s where they a variety of problems and data sources. could help scientists harness biological light-colored elements stand.” To further refine feature selection, He still contributes to its development. molecules for specific purposes or build denoting high degrees of the program includes a fifth parameter: Sisto appreciated the practicum’s their own. similarity and dark colors noise, or a measure of random data variation. introduction to data mining. “It was a great Sisto’s techniques capture the strange indicating low similarity. If certain features rank lower than way to learn about it from Chandrika, who effects of quantum mechanics and work Image courtesy of Aaron Sisto. this noise baseline, then Sisto could throw has years of experience. I think I got a neat from first principles: starting without out those features, further reducing the perspective on the applied side.” For her preset conditions or empirical data. Such parameter space. “We know anything part, Kamath says she had hoped Sisto ab initio calculations can track the complex ranked below random noise is absolutely “would at least learn something about interactions between electrons in atoms.
P14 DEIXIS 15 DOE CSGF ANNUAL DEIXIS 15 DOE CSGF ANNUAL P15 alumni profiles
FROM ALUMNI TO LEADERS
Helping Users Get Matthew Norman Oak Ridge National Laboratory Science From Titan
WHEN MATTHEW NORMAN STARTED as a reproducing and solving problems. “It’s making sure (researchers) scales better and can run on more processors than other versions. computational climate scientist at Oak Ridge National Laboratory can get science out of the code as efficiently as possible.” Porting CAM-SE to run on GPUs produced a speedup of more in 2011, he thought he may be unqualified to work with Titan, one In a similar vein, Norman oversaw porting the Community than two times compared to running on Titan’s CPUs alone for of the world’s biggest, most powerful machines. Atmosphere Model-Spectral Element (CAM-SE) to Titan. CAM-SE that science target. “I had my worries coming in that I wouldn’t necessarily know is part of the larger Community Climate System Model in DOE’s With his INCITE, CAM-SE and other research work, Norman has wwhat to do, but then I quickly realized that no one does until they Accelerated Climate Modeling for Energy program. The Spectral become known as a “GPU guy” within the climate and high-performance get here,” says Norman, a Department of Energy Computational Element Method (SEM) variant requires less parallel communication computing communities: someone skilled at tailoring algorithms to Science Graduate Fellowship (DOE CSGF) recipient from 2008 to than other CAM versions as the processors calculate physical run on a combination of accelerators and CPUs. He doesn’t mind the 2011. “Everyone has an idea of how you debug a code until you’re changes in each part of the atmosphere. That means CAM-SE label, but Norman would rather be known for designing and refining doing it on the order of 10,000 nodes. It completely changes the scientific computing algorithms to increase efficiency and accuracy. way you need to approach it.” Four years later, Norman qualifies as something of an old TACKLING A TIME STEP hand as he helps adapt codes to run on Titan, a Cray XK7 at the In one project, Norman addresses a SEM weakness. Compared Oak Ridge Leadership Computing Facility. Titan’s architecture to other techniques in its class, the method progresses calculations includes a combination of standard central processing units Contour plot of potential temperature (in degrees through time in large steps, but the time step gets drastically (CPUs) and graphics processing units (GPUs), video-game chip Kelvin, with red the warmest and blue the coldest) for a shorter when calculations are cast in higher-order configurations siblings that accelerate calculations. The mix makes it tricky for two-dimensional rising thermal in a neutrally stratified designed to cut error. “Reducing the time step means it takes existing programs to run on Titan. atmosphere at high resolution with selective damping to longer to get to the final solution, so you want to avoid that.” Norman’s experience is tested as a liaison to researchers avoid spurious oscillations (leading to the smaller eddies Norman’s modifications to a previously developed technique, seen in the plot). This test is designed to tell researchers awarded computer time from the Department of Energy’s however, allow for higher-order calculations without reducing the whether their numerical method gives the correct answer Innovative and Novel Computational Impact on Theory and time step. It’s a difficult problem, but “you get a very large time before it’s integrated into a full climate simulation code. Experiment (INCITE) program. It grants millions of computer step, very high order, very low communication, pretty much all in processor hours on Titan and other DOE supercomputers to one” algorithm. He’s demonstrated the method in one-dimensional This test of a climate simulation numerical method shows a scientists from government, academia and industry. Norman problems and is working on extending it to two dimensions. That’s volume rendering of potential temperature sliced down the y-axis and his colleagues are essentially embedded in research teams proven more difficult, he says, but “I’ve got some tricks up my sleeve.” with streamlines to show wind directions for a three-dimensional to help codes produce science on Titan. Norman started as an undergraduate meteorology major at North rising thermal in a neutrally stratified atmosphere. The colors Carolina State University in his native state and thought he might denote temperature in degrees Kelvin, with red the warmest and blue the coldest. Images courtesy of Matthew Norman. OVERCOMING OBSTACLES do television weather forecasting. He soon dropped those plans, “I’ve worked on earthquake hazard codes and climate however, after discovering he liked math more than meteorology. codes and even a quantum Monte Carlo code,” Norman says. When he’s not wrestling with algorithms, Norman usually is Each has its own obstacles to running on Titan – ones Norman home with his wife, Shannon, and the latest in a series of foster tries to conquer by profiling the program to see where it expends children they’ve hosted. While there are challenges in getting resources, such as on communication or system overhead, or by children into permanent homes, hopefully with their birth parents, “the reward is just getting to spend life with these great kids,” Norman adds.
P16 DEIXIS 15 DOE CSGF ANNUAL DEIXIS 15 DOE CSGF ANNUAL P17 alumni profiles
Amoolya Singh Amyris Inc.
Creating Tiny Factories
IT’S DIFFICULT TO DECIDE what she likes best It’s another aspect of her work that Singh prizes. “It’s very rare about her work at Amyris Inc., Amoolya Singh says. There are so to come across places that have so much data of so many different many rewarding aspects. dimensions and units and to think about how to integrate all that.” Perhaps it’s “the impact and the potential to do something Each week, Singh and her team help Amyris biologists decide that benefits not only our immediate needs for society, but also which of the thousands of new strains should advance to larger- imight result in a cleaner future” for her twin 5-year-old boys, says scale fermentation tests. “Running a 200,000-liter fermenter Singh, a Department of Energy Computational Science Graduate is an expensive proposition,” she adds, so getting it right is Fellowship (DOE CSGF) recipient from 2002 to 2006. imperative. Her team uses algorithms that analyze multiple Singh heads the Scientific Computing Group at Amyris variables simultaneously to infer which properties can best predict (pronounced AMerus), based in California’s Bay Area. The company how a yeast strain will perform at scale. engineers microorganisms – mostly yeasts – to ferment sugars for “We have databases and algorithms that handle these routinely a range of biorenewable products, including drugs, fuels, flavors, in almost a touchless fashion,” Singh says, but humans also analyze fragrances and tire components. Today most such substances are a fraction of the results. “The algorithm has false positives and made from petroleum. false negatives, and we can use a hybrid approach with people as “Every kind of plastic and synthetic material that’s around you, well as algorithms” to ensure accuracy. Besides seeking the most we may have a hook into making” from renewable feedstocks, promising yeast strains, the researchers also target particular Singh says. Amyris believes its products will offer alternatives to properties, such as yield. many oil-based products within a decade. Most of the computations run on the company’s in-house Linux machines. The Scientific Computing Group sends more PUMPING OUT STRAINS demanding calculations to cloud computing applications. Singh’s The scientific challenge also is rewarding, Singh says. The department divides its time between analysis and devising company’s automated strain engineering platform pumps out analytical tools written in the Python, R and F# languages In the end, she came to the United States to pursue biology This microscope image shows Amyris Inc.’s genetically engineered around 30,000 new yeast genotypes per week. Researchers test and the SQL database management language. and computer science at Carnegie Mellon University. She later yeast cells producing farnesene, an oily 15-carbon molecule each strain’s fermentation capacity at lab scale, usually in high- The most promising yeast strains move on to nine-day fermenter earned a DOE CSGF and graduated with a doctorate in computational used for making tire additives, lubricants, cosmetics and fuels. throughput plastic plates with 96 300-microliter wells in each. runs at greater capacity. Ultimately the best ones make it to the biology from the University of California, Berkeley. The dark brown blobs are farnesene, the bluish granules are Tests measure the strains’ health and viability, what the strains company’s production fermentation facility in Brazil. Singh credits the fellowship’s curriculum requirements with yeast and the gray areas are sugar. Image courtesy of Amyris. produce, product in proportion to sugar consumed, and greatly strengthening her training and preparing her for her current undesirable byproducts. FROM MUSIC TO BIOLOGY duties. She also made many friends with other fellows at the DOE “You can imagine that each strain has multiple measurements Singh’s parents are biology professors in her home country of CSGF annual program review, which was “super valuable,” she says. taken at multiple points in time, so that’s a really massive data set India, but it wasn’t clear she would go into that field. She studied “It was almost a relief to meet other people” who were to look through,” Singh says. “Using all this information together music all her life and pondered it as a profession. She also considered working at the science-computing interface “and see that we we can try to predict how a strain would perform at greater and a career in architecture. had so much in common.” greater volumes.”
P18 DEIXIS 15 DOE CSGF ANNUAL DEIXIS 15 DOE CSGF ANNUAL P19 alumni profiles
John Dolbow Duke University
Extending a Method happens in engine components. It works best when the geometry of ideas,” Dolbow says. The paper that he, Moës and Belytschko doesn’t change dramatically. The FEM is powerful, but is limited published 15 years ago has more than 3,000 citations. for Collaboration “when we run into problems where there’s an evolving geometry that’s central to the problem.” EXTENDING THE EXTENSION Fractures fall into the problematic category. They have sharp Since then, Dolbow has worked on improving the XFEM and WITH HIS BACKGROUND, John Dolbow could define Dolbow helped develop as a Northwestern University graduate features, they’re irregular, and they can break the modeled domain applying it to new problems. He’s modeled evolving interfaces, himself as an engineer, a mathematician or a computer scientist. student. It’s become his main tool to investigate phenomena as into separate subdomains as time progresses. especially boundaries between two material phases, and fluids as But the Duke University professor thinks of himself as a computational diverse as fractures, soft-wet materials and the interactions In the late 1990s, Dolbow’s advisor, Ted Belytschko, and fellow they interact with structures. scientist first, a title that best encapsulates the combination of fields between fluids and structures. student Tom Black laid the groundwork for what became the XFEM. On a recent sabbatical at Sandia National Laboratories in New he works in. “On a daily basis, what I am really doing is a mix of As the name suggests, XFEM is an enhancement of the Finite Dolbow, with postdoctoral researcher Nicolas Moës, worked to make Mexico, Dolbow worked with staff members to improve codes wengineering, applied math and computer science,” says Dolbow, a Elements Method (FEM), a mathematical technique for discretization: Black’s developments simpler for computer scientists to implement. simulating large-scale fragmentation. “What I’m interested in Department of Energy Computational Science Graduate Fellowship the computational process of dividing an object or an area with a Finite element users typically shape mesh elements to what are situations in which the loading on the system is very fast,” recipient from 1997 to 1999. mesh of elements so a computer can calculate the physical processes they’re trying to model. “We came to realize that approach is really changing it “from a state where things are highly connected Dolbow’s unusual appointment at Duke reflects his broad happening in each. When reassembled, the elements portray the very arbitrary — that it wasn’t necessary,” Dolbow says. He and his to one where things are highly disconnected.” It’s “a very interests. He’s in three departments: civil and environmental entire object or area. It’s similar to how thousands of pixels colleagues concluded they only needed to ensure the mesh covered challenging computational science problem.” engineering, mechanical engineering and materials science, and comprise a digital image. the domain under consideration. By adding mathematical functions Now Dolbow is helping shape the future of computational mathematics. That lets Dolbow recruit graduate students from and modifying the algorithm that integrates the elements, they science in the Department of Energy. He’s one of the newest each department for interdisciplinary research. CHANGING GEOMETRY could model an evolving system, like a widening fracture. members of the Advanced Scientific Computing Advisory What connects many of these subjects: the extended Finite FEM, Dolbow says, was designed partly “to deal with problems “I think, philosophically, that was a big breakthrough in the Committee (ASCAC), a group of experts that helps guide the Element Method (XFEM), a major computational science advancement where the geometry was fairly complex,” like modeling what way the Finite Element Method was viewed, and it generated a lot Department of Energy’s Advanced Scientific Computing Research (ASCR) program. The biggest issue confronting the committee and ASCR, Dolbow says, is exascale computing: machines capable of speeds a thousand times faster than today’s best. With exascale, the computer architecture that largely sits in the background in developing computational science codes will push to the forefront. Many national laboratories’ codes have decades of history, and “one of the challenges is going to be figuring out how best to adapt those to work on exascale platforms.” Dolbow had to call in for his first ASCAC meeting. In fall 2014 he and his family (wife Alice and daughters Zoe, 4, and Maddox, 8) were in Japan to serve the first part of his sabbatical, at the Okinawa Institute of Science and Technology.
Simulation of dynamic crack propagation using the eXtended Finite Element Method (XFEM). Image courtesy of John Dolbow.
P20 DEIXIS 15 DOE CSGF ANNUAL DEIXIS 15 DOE CSGF ANNUAL P21 essay contest WINNING CYSE ENTRY COMMUNICATE YOUR SCIENCE & ENGINEERING CONTEST
ANDREW STERSHIC BUILDING BATTERIES FROM THE MICROSTRUCTURE UP
A Discrete Element Method simulation of lithium-ion cathode particles as they’re pressed during manufacturing. This model captures the motion TIRES SQUEAL BEFORE A LOUD batteries to provide power day and night. of every particle but does not describe their cracking or splitting. Image crash. Car parts, antifreeze and glittering Electric utilities can use similar devices to courtesy of Andrew Stershic, from A.J. Stershic, S. Simunovic, and J. Nanda. glass litter the street. An iPad hits a make renewable energy a more dependable “Modeling the Evolution of Lithium-ion Particle Contact Distributions using RECOGNIZING sidewalk. Cracks erupt like a spider option for millions of users. The battery a Fabric Tensor Approach.” Submitted to Journal of Power Sources. web across the device’s screen. market exceeds $100 billion annually, with WRITING SKILL We understand pretty well how lithium-ion grabbing an increasing share. everyday brittle materials – ones that Despite our growing need for them, crack after even tiny deformations – break. however, lithium-ion batteries aren’t as safe For 10 years, the DOE TBut scientists have a hazier picture of or efficient as they could be. Recall the news If we look deep inside them, we can have a harder time traveling through the cathode microstructure at different stages CSGF has staged a competition another vital fracture phenomenon: how stories about Tesla cars erupting into see what’s going on. There are two sides, cathode skeleton to the outer plate, then of manufacturing. We’re also preparing an designed to give current and brittle particles in lithium-ion batteries flames after minor collisions? And the anode and cathode, represented by the battery doesn’t store and supply as experiment to load individual particles former fellows an opportunity crack. These batteries power our modern on a smaller scale, damage during minus and plus signs. Each is made of a much power. and record their failure pressures and to write about their work with a world; they are ubiquitous in iPhones and manufacturing robs us of storage skeleton of particles, and lithium ions (Li+ Cracks in the battery particles can crack patterns. Comparing these tests to broader, non-technical audience electric cars alike. Their high power and performance and prematurely shortens for chemistry folks) flow in one direction get worse as the battery is used. During numerical models helps us fine-tune their in mind. The contest encourages ample charge capacity also make them battery life. or the other, depending on whether the charging and discharging, the cathode input parameters. We can then evaluate communication of the value of contenders for large-scale energy storage. What if the same techniques we use battery is charging or discharging. The particles correspondingly shrink and grow, how likely specific loads are to crack computation and computational For example, Tesla Motors founder Elon to model cracks in glass also could help cathode microstructure is made of causing some small cracks from the initial particles. With this information, we can science and engineering research Musk unveiled a system in April that can make lithium-ion batteries safer and millions of spherical particles of a brittle pressing to enlarge, splitting the particles. evaluate alternative materials and to society. store solar energy in large household more powerful? metal oxide containing materials such as This is one reason battery life gets worse manufacturing processes to reduce In addition to recognition and lithium, cobalt, nickel and manganese. with age. particle splitting, making batteries a cash prize, winners receive The particles must be sandwiched The standard method for computationally more powerful and efficient. the opportunity to work with a between two metal sheets before the modeling battery microstructure geometry There’s a great opportunity to improve professional science writer to battery can be rolled into the familiar portrays each particle individually but at a lithium-ion batteries by developing and critique and copy-edit their cylindrical shape. They’re laid in a wet low accuracy. Because there are millions of applying numerical models that simulate entries. This year’s winner is The application of a leading fracture paste onto the sheet and pressed to the them, it’s difficult to do more, and so these failure by cracking. This also improves Andrew Stershic, a fourth-year model to simulate cracking starting desired thickness. models imprecisely predict or represent mechanical modeling in a number of at the corner of a concrete bracket. fellow in civil engineering and This is where the problem begins: events like particle cracking. fields, enhancing technology development. Image courtesy of Andrew Stershic. computational mechanics at Because the cathode particles are so But by using computational failure Creating more powerful batteries will have Duke University. brittle, some crack under the pressure. models that simulate cracks on larger far-reaching impacts on the consumer For more information on the The battery works by exchanging lithium objects – like windowpanes and iPad electronics and automotive industries of Communicate Your Science ions between the cathode and anode and screens – we can identify and predict today and will enable development of & Engineering Contest, visit sending electrons to the outer plates. loading cases that make lithium-ion industries we’ve never imagined tomorrow. www.krellinst.org/csgf/outreach/ When particles crack and split, their battery particles fail. For our study, cyse-contest. interconnectivity weakens. If electrons we have great experimental data of the
P22 DEIXIS 15 DOE CSGF ANNUAL DEIXIS 15 DOE CSGF ANNUAL P23 howes scholar
The Frederick A. Howes Scholar in Computational Science award was established in 2001 to honor the late Frederick Anthony Howes, who was a champion for computational science education.
QUANTUM PRODIGY ABOUT FRED HOWES MATTHEWS RECOGNIZED FOR LEADERSHIP IN COMPUTATIONAL CHEMISTRY
In the 14 years since it was first conferred, the Frederick A. Howes Scholar in Computational Science award has become emblematic of research excellence and outstanding leadership. It’s a fitting tribute to Howes, who was known for his scholarship, intelligence and humor. As a high school sophomore, Devin and Sciences at UT-Austin. He says his The idea is that “if you have very efficient Howes earned his bachelor’s and doctoral degrees Matthews developed a computer program computer science experience began with tensor contraction, you can make everything in mathematics at the University of Southern California. that attempted to solve the famed Schrödinger his 2011 practicum at Argonne National efficient across the board by simply plugging” He held teaching posts at the universities of Wisconsin equation, which describes an atomic system’s Laboratory, where he collaborated with in that method, Matthews says. and Minnesota before joining the faculty of the University quantum mechanical state. Edgar Solomonik, a DOE CSGF recipient In most cases, quantum chemistry of California, Davis, in 1979. Ten years later Howes By the time Matthews began at the now at Switzerland’s ETH Zurich. Together, programs include code for all operations, served a two-year rotation with the National Science AUniversity of Texas at Austin three years they tackled tensor contraction, a common including tensor contraction. Aquarius Foundation’s Division of Mathematical Sciences. He joined DOE in 1991. later, he knew more about quantum operation found in quantum chemistry users, however, can plug in a tensor In 2000, colleagues formed an informal committee chemistry than many new graduate calculations, especially methods known contraction method they believe is best to honor Howes. They chose the DOE CSGF as the students. A chemistry and biochemistry as coupled cluster. Tensors organize data or more useful for a specific application. vehicle and gathered donations, including a generous professor, John Stanton, was so impressed with multiple dimensions. For example, a Aquarius uses CTF, Matthews says, but contribution from Howes’ family, to endow an award he brought Matthews, still just a freshman, matrix is a two-dimensional tensor. he’s working to support other techniques. in his name. into his research group. Quantum chemistry is key to Matthews went on to win a Department understanding the details of atomic and INTERCHANGEABLE EMPHASIS of Energy Computational Science Graduate molecular interactions. Coupled cluster Researchers are using Aquarius, but Fellowship (DOE CSGF) and to earn a 2014 methods describe these complex many- Matthews also wants them to embrace its chemistry Ph.D., with Stanton supervising. His body problems, in which particles – approach. “If people adopted the philosophy high-precision, high-efficiency algorithms and electrons, in this case – influence each I’ve put into it and make software work software are making significant contributions other. The methods yield an approximate together and be interchangeable, that He finds academia the most attractive but PAST HOWES SCHOLARS to computational quantum chemistry. solution to the Schrödinger equation. would be a really positive impact” on would consider an industrial post. A desire For his technical achievements, At its highest level, Matthews says, computational science. to be near family in the Austin area also leadership and character, a committee of computational chemistry captures the details In its citation, the Howes selection will influence his choices. 2014 Hayes Stripling IV DOE CSGF alumni and friends named of atomic and molecular interactions, committee wrote that Matthews’ leadership Regardless, Matthews says he’ll 2013 Ashlee Ford Versypt Matthews the 2015 Frederick A. Howes including electron arrangements, as on Aquarius “speaks highly to his willingness continue supporting the DOE CSGF. His 2012 Carolyn Phillips and Matthew Reuter Scholar in Computational Science. accurately as experiments. But “those are to challenge the bounds of state-of-the-art advice to new recipients: Maximize the 2011 Alejandro Rodriguez Howes, manager of DOE’s Applied heavyweight calculations. In the future in computation, to take responsibility, and program’s features. “It’s not just paying a 2010 Julianne Chung Mathematical Sciences Program, was an the idea is to exceed the accuracy of the to follow through with dedication and drive.” stipend and paying your tuition. It’s giving 2009 David Potere advocate for the fellowship and for experiments or to do calculations where we Matthews also has distinguished you opportunities to make connections 2008 Mala Radhakrishnan Devin Matthews of the University of computational science. Friends founded simply can’t do an experiment yet – to be himself as a mentor and leader, the and do new things and learn to work with 2007 Jaydeep Bardhan and Texas at Austin accepts the Howes the award after he died at age 51 in 1999. really predictive.” committee noted, by initiating projects and people from other fields.” Kristen Grauman award from selection committee Matthews received his honorarium Matthews and Solomonik, with Argonne supervising and assisting fellow graduate 2006 Kevin Chu and chairman David Brown, director of the and award in July at the fellowship’s annual intern Martin Schatz, devised the Cyclops students and colleagues. “Devin’s Matthew Wolinsky Computational Research Division at program review in Arlington, Virginia. Tensor Framework (CTF), a method that demonstrated excellence in research and 2005 Ryan Elliott and Judith Hill Lawrence Berkeley National Laboratory. exploits symmetries in tensor data to perform leadership in the computational quantum 2004 Collin Wick A COMPUTER SCIENCE INTRO contractions more quickly and efficiently chemistry community exemplifies the 2003 Oliver Fringer and Matthews now is a postdoctoral than other approaches. qualities that Fred Howes encouraged Jon Wilkening 2001 Jeffrey Hittinger and researcher in the Science of High- Cyclops plays a role in one of Matthews’ in all young scientists,” the citation says. Mayya Tokman Performance Computing Group and the latest projects: Aquarius, a framework for Matthews’ accomplishments have Institute for Computational Engineering quantum chemistry tensor computation. opened numerous career opportunities.
P24 DEIXIS 15 DOE CSGF ANNUAL DEIXIS 15 DOE CSGF ANNUAL P25 FELLOWS: MAJOR DISCIPLINES DOE CSGF FELLOWS: DISTRIBUTION BY DISCIPLINE*
Students who enter the Department of Energy Computational Science Graduate Fellowship must combine 19% Engineering courses in applied mathematics and computer science with courses in specific application disciplines, preparing them to apply high-performance computing in a range of fields. The accompanying graphic groups fellows into four broad areas, but they study a diversity of specific subjects. 19% Computer Science & Mathematics Graduates go on to take leadership positions in industry, academia and government laboratories, helping the United States address vitally important problems. The DOE CSGF is supported by the DOE Office of Advanced Scientific Computing Research (ASCR) within 26% Biology & Bioengineering the Office of Science and the Advanced Simulation and Computing (ASC) program within the National Nuclear Security Administration. For complete lists of fellows and alumni (by last name, Ph.D. institution, fellowship start year, practicum 36% Physical Sciences location, current location and area of study), go to www.krellinst.org/csgf .
*For 2014-15 Academic Year
CLASS OF 2015
Jason Bender Curtis Lee Aurora Pribram-Jones Joshua Vermaas University of Minnesota Duke University University of California, Irvine University of Illinois at Urbana-Champaign Hypersonic Computational Fluid Dynamics Computational Mechanics Theoretical Chemistry Biophysics Advisor: Graham Candler Advisor: John Dolbow Advisor: Kieron Burke Advisor: Emad Tajkhorshid Practicum: Argonne National Laboratory Practicum: Lawrence Berkeley National Laboratory Practicum: Sandia National Laboratories, New Mexico Practicum: National Renewable Energy Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected] Contact: [email protected]
Rogelio Cardona-Rivera Sarah Loos Alexander Rattner Matthew Zahr North Carolina State University Carnegie Mellon University Georgia Institute of Technology Stanford University Artificial Intelligence Verification of Hybrid Systems Mechanical Engineering Computational and Mathematical Engineering Advisor: R. Michael Young Advisor: Andre Platzer Advisor: Srinivas Garimella Advisor: Charbel Farhat Practicum: Sandia National Laboratories, New Mexico Practicum: Oak Ridge National Laboratory Practicum: Idaho National Laboratory Practicum: Lawrence Berkeley National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected] Contact: [email protected]
Phoebe DeVries Heather Mayes Michael Rosario Harvard University Northwestern University Duke University Earth Science Chemical Engineering Evolutionary Biomechanics Advisor: Brendan Meade Advisor: Linda Broadbelt Advisor: Sheila Patek Practicum: Lawrence Berkeley National Laboratory Practicum: National Renewable Energy Laboratory Practicum: Sandia National Laboratories, California Contact: [email protected] Contact: [email protected] Contact: [email protected]
Omar Hafez Jarrod McClean Hansi Singh University of California, Davis Harvard University University of Washington Computational Solid Mechanics Chemical Physics Atmosphere-Ocean Physics Advisor: Mark Rashid Advisor: Alan Aspuru-Guzik Advisor: Cecilia Bitz Practicum: Lawrence Livermore National Laboratory Practicum: Los Alamos National Laboratory Practicum: Pacific Northwest National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected]
Maxwell Hutchinson Robert Parrish Chris Smillie University of Chicago Georgia Institute of Technology Massachusetts Institute of Technology Physics Theoretical Chemistry Biology, Computer Science and Bioengineering Advisor: Robert Rosner Advisor: David Sherrill Advisor: Eric Alm Practicum: Lawrence Berkeley National Laboratory Practicum: Lawrence Livermore National Laboratory Practicum: Lawrence Berkeley National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected]
P26 DEIXIS 15 DOE CSGF ANNUAL DEIXIS 15 DOE CSGF ANNUAL P27 2013 FELLOWS DIRECTORY 2015
4 TH YEAR FELLOWS 3 RD YEAR FELLOWS
Front, left to right: Miles Lubin, Sherwood Richers, Brian Powell*, Dragos Velicanu, Victor Minden and Andrew Till; Front, left to right: Alexander Turner, Nicholas Frontiere, Adam Richie-Halford and Daniel Rey; Middle, left to right: Melissa Yeung, Jamie Smedsmo*, Daniel Strouse, Andrew Stine, Brenhin Keller, Sarah Middleton, Back, left to right: Isha Jain, David Plotkin, David Ozog, Chelsea Harris and Kathleen Alexander. Justin Lee and Britni Crocker; Back, left to right: Eileen Martin, Eric Isaacs, Thomas Catanach, Samuel Blau, Derek Macklin, Andrew Stershic and Jesse Lopez. *Withdrew
Samuel Blau Justin Lee Sarah Middleton Daniel Strouse Kathleen Alexander Isha Jain David Plotkin Adam Richie-Halford Harvard University Massachusetts Institute of Technology University of Pennsylvania Princeton University Massachusetts Institute of Technology Massachusetts Institute of Technology University of Chicago University of Washington Chemical Physics Computational Imaging/Biomedical Optics Genomics and Computational Biology Theoretical Neuroscience Computational Materials Science Computer Science and Systems Biology Earth Sciences Physics Advisor: Alan Aspuru-Guzik Advisor: George Barbastathis Advisor: Junhyong Kim Advisor: William Bialek Advisor: Christopher Schuh Advisor: Vamsi Mootha Advisor: Dorian Abbot Advisor: Aurel Bulgac Practicum: Argonne National Laboratory Practicum: Lawrence Berkeley Practicum: Lawrence Berkeley Practicum: Lawrence Berkeley Practicum: Oak Ridge National Laboratory Contact: [email protected] Practicum: Argonne National Laboratory Practicum: Brookhaven National Laboratory Contact: [email protected] National Laboratory National Laboratory National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected] Contact: [email protected] Contact: [email protected] Contact: [email protected] David Ozog Thomas Catanach Nicholas Frontiere University of Oregon Daniel Rey Alexander Turner California Institute of Technology Jesse Lopez Victor Minden Andrew Till University of Chicago Computational Science University of California, San Diego Harvard University Applied and Computational Mathematics Oregon Health and Science University Stanford University Texas A&M University Physics Advisor: Allen Malony Biophysics Atmospheric Science Advisor: Jim Beck Environmental Science and Engineering Computational and Mathematical Engineering Multiphysics Scientific Computational Advisor: David Reid Practicum: Argonne National Laboratory Advisor: Henry Abarbanel Advisor: Daniel Jacob Practicum: Los Alamos National Laboratory Advisor: Antonio Baptista Advisor: Lexing Ying Nuclear Engineering Practicum: Lawrence Livermore Contact: [email protected] Practicum: Lawrence Livermore Practicum: Lawrence Berkeley Contact: [email protected] Practicum: Argonne National Laboratory Practicum: Lawrence Berkeley Advisor: Marvin Adams National Laboratory National Laboratory National Laboratory Contact: [email protected] National Laboratory Practicum: Los Alamos National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected] Britni Crocker Contact: [email protected] Contact: [email protected] Massachusetts Institute of Technology Miles Lubin Chelsea Harris Computational Neuroscience Massachusetts Institute of Technology Sherwood Richers Dragos Velicanu University of California, Berkeley Advisor: Sydney Cash Operations Research California Institute of Technology Massachusetts Institute of Technology Astrophysics Practicum: Los Alamos National Laboratory Advisor: Juan Pablo Vielma Astrophysics High Energy Physics Advisor: Peter Nugent Contact: [email protected] Practicum: Los Alamos National Laboratory Advisor: Christian Ott Advisor: Gunther Roland Practicum: National Renewable Contact: [email protected] Practicum: Lawrence Berkeley Practicum: Lawrence Berkeley Energy Laboratory Eric Isaacs National Laboratory National Laboratory Contact: [email protected] Columbia University Derek Macklin Contact: [email protected] Contact: [email protected] Applied Physics Stanford University Advisor: Chris Marianetti Computational and Systems Biology Andrew Stershic Melissa Yeung Practicum: Brookhaven National Laboratory Advisor: Markus Covert Duke University California Institute of Technology 2015 FELLOWS DIRECTORY Contact: [email protected] Practicum: Lawrence Berkeley Civil Engineering/Computational Mechanics Mathematics National Laboratory Advisor: John Dolbow Advisor: Mathieu Desbrun Brenhin Keller Contact: [email protected] Practicum: Oak Ridge National Laboratory Practicum: Lawrence Berkeley Princeton University Contact: [email protected] National Laboratory Geochemistry and Geochronology Eileen Martin Contact: [email protected] Advisor: Blair Schoene Stanford University Andrew Stine Practicum: Lawrence Berkeley Computational and Northwestern University National Laboratory Mathematical Engineering Chemical and Biological Engineering Contact: [email protected] Advisor: Biondo Biondi Advisor: Linda Broadbelt Practicum: Lawrence Livermore Practicum: Argonne National Laboratory National Laboratory Contact: [email protected] Contact: [email protected]
P28 DEIXIS 15 DOE CSGF ANNUAL DEIXIS 15 DOE CSGF ANNUAL P29 2015 FELLOWS DIRECTORY
2 ND YEAR FELLOWS 1 ST YEAR FELLOWS
Front, left to right: Thomas Anderson, Jonathan Gootenberg, Alnur Ali, Kathleen Weichman, Kyle Felker, Joy Yang, Jay Stotsky, Front, left to right: Ian Dunn, Helena Qi, Hannah Klion, Casey Berger, Noah Mandell; Hilary Egan, Danielle Rager, Hannah De Jong, Adam Sealfon, Thomas Holoien; Middle, left to right: Jordan Hoffmann, Morgan Hammer, Back, left to right: Zane Crawford, Maximilian Bremer, Carson Kent, Richard Barnes. Aditi Krishnapriyan, Gerald Wang; Back, left to right: Mukarram Tahir, Julian Kates-Harbeck, Adam Riesselman, Alexander Kell. Not pictured: Nicholas Boffi and Emmet Cleary. Not pictured: Will Fletcher, Ryan McKinnon, Thomas Thompson and Alexander Williams.
Alnur Ali Jonathan Gootenberg Aditi Krishnapriyan Mukarram Tahir Richard Barnes Maximilian Bremer Ian Dunn Noah Mandell Carnegie Mellon University Harvard University Stanford University Massachusetts Institute of Technology University of California, Berkeley University of Texas at Austin Columbia University Princeton University Machine Learning Computational Biology Condensed Matter Physics Materials Science and Engineering Ecology, Evolution and Behavior Computational Mathematics Chemical Physics Plasma Physics Advisor: Zico Kolter Advisor: Feng Zhang and Materials Science Advisor: Alfredo Alexander-Katz Advisor: Clarence Lehman Advisor: Clint Dawson Advisor: David Reichman Advisor: Greg Hammett Practicum: Lawrence Berkeley Contact: [email protected] Advisor: Evan Reed Practicum: Argonne National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected] Contact: [email protected] National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected] Morgan Hammer Casey Berger Emmet Cleary Carson Kent Helena Qi University of Illinois at Urbana-Champaign Ryan McKinnon Thomas Thompson University of North Carolina, Chapel Hill Princeton University Stanford University Massachusetts Institute of Technology Thomas Anderson Theoretical Chemistry Massachusetts Institute of Technology Harvard University Theoretical and Computational Physics Mechanical and Aerospace Engineering Computational and Mathematical Engineering Chemistry California Institute of Technology Advisor: Sharon Hammes-Schiffer Physics Geophysics Advisor: Joaquin Drut Advisor: Michael Mueller Advisor: Paul Constantine Advisor: Heather Kulik Applied and Computational Mathematics Contact: [email protected] Advisor: Mark Vogelsberger Advisor: Brendan Meade Contact: [email protected] Contact: [email protected] Contact: [email protected] Contact: [email protected] Advisor: Oscar Bruno Practicum: Lawrence Berkeley Practicum: Oak Ridge National Laboratory Contact: [email protected] Jordan Hoffmann National Laboratory Contact: [email protected] Nicholas Boffi Zane Crawford Hannah Klion Harvard University Contact: [email protected] Harvard University Michigan State University University of California, Berkeley Hannah De Jong Applied and Computational Mathematics Gerald Wang Condensed Matter Physics Electromagnetics Computational Astrophysics Stanford University Advisor: Chris Rycroft Danielle Rager Massachusetts Institute of Technology Advisor: Tamar Seideman Advisor: Shanker Balasubramaniam Advisor: Eliot Quatert Genetics Contact: [email protected] Carnegie Mellon University Mechanical Engineering Contact: [email protected] Contact: [email protected] Contact: [email protected] Advisor: Euan Ashley Neural Computation Advisor: Nicolas Hadjiconstantinou Contact: [email protected] Thomas Holoien Advisor: Valerie Ventura Practicum: Argonne National Laboratory The Ohio State University Practicum: Lawrence Berkeley Contact: [email protected] Hilary Egan Astronomy National Laboratory University of Colorado Advisor: Krzysztof Stanek Contact: [email protected] Kathleen Weichman Astrophysics Contact: [email protected] University of Texas at Austin Advisor: Jack Burns Adam Riesselman Physics Contact: [email protected] Julian Kates-Harbeck Harvard University Advisor: Michael Downer Harvard University Bioinformatics and Integrative Genomics Contact: [email protected] Kyle Felker Physics Advisor: Peter Park
Princeton University Advisor: Mara Prentiss Contact: [email protected] Alexander Williams 2015 FELLOWS DIRECTORY Applied and Computational Mathematics Contact: [email protected] Stanford University Advisor: James Stone Adam Sealfon Computational Neuroscience Contact: [email protected] Alexander Kell Massachusetts Institute of Technology Advisor: Timothy Gentner Massachusetts Institute of Technology Computer Science Contact: [email protected] Will Fletcher Computational Neuroscience Advisor: Piotr Indyk Stanford University Advisor: Nancy Kanwisher Practicum: Lawrence Berkeley Joy Yang Biophysics Contact: [email protected] National Laboratory Massachusetts Institute of Technology Advisor: Vijay Pande Contact: [email protected] Computational and Systems Biology Contact: [email protected] Advisor: Eric Alm *Entered 2013; Deferred Two Years Jay Stotsky Contact: [email protected] University of Colorado Applied Mathematics Advisor: David Bortz P30 DEIXIS 15 DOE CSGF ANNUAL Contact: [email protected] DEIXIS 15 DOE CSGF ANNUAL P31 The Krell Institute 1609 Golden Aspen Drive, Suite 101 Ames, IA 50010 (515) 956-3696 Funded by the Department of Energy Office of Science www.krellinst.org/csgf and the National Nuclear Security Administration.