recent trends in computational catalysis

N A N O S C A L E M A T E R I A L S

The past twenty years have brought incredible expansion of capabilities andpossibilities to all aspects of computing, and the computational catalysis field is noexception. Electronic structure calculations are now readily used to providecomputational insight into the chemical and physical processes that determine theproperties of different types of catalysts.

Important advances in both physical simulationalgorithms and the development of massively par-allel computers have greatly expanded the classesof catalytic problems that can be treated with elec-tronic structure-based approaches. Researchersat Argonne National Laboratory (ANL) are devel-oping efficient catalytic processes and are tacklingquestions of energy production, usage, and secu-rity. ANL’s emphasis is on both the physical/chem-ical nature of the problems, as well as thecomputational software and hardware deployed.

As a field, catalysis spans a significant range oflength scales—catalysts can be composed of any-thing from single metal atoms surrounded byorganic ligands (homogeneous catalysts), to clus-ters of a few dozen metal, oxide, carbide, or nitrideatoms (subnanometer heterogeneous catalysts),to metal particles of up to 10 nm or more in diam-eter (nanoparticle heterogeneous catalysts). Sub-nanometer and heterogeneous catalysts aregenerally supported on other complex materials(figure 1), and this adds complexity. Although thesupport generally plays a secondary role in the cat-alytic chemistry, the choice of this material, insome cases, can affect the course of the reactions.

Each class of catalysts has unique properties thatare useful in different situations. Homogeneouscatalysts (which are not treated extensively herein)can demonstrate a remarkable ability to tune

product distributions to favor desired reactionproducts (they are very selective; sidebar “GeneralPrinciples of Catalysis,” p50). They may also showa variety of other unique catalytic properties stem-ming from the molecular-like nature of the cata-lysts’ electronic orbitals. The drawback of this classof catalysts is they cannot be easily immobilized,and therefore they require sophisticated process-ing technologies to function efficiently.

Heterogeneous catalysts, on the other hand,have traditionally shown less selectivity, but theyare easy to synthesize and process, and areextremely robust under a variety of reaction con-ditions. Subnanometer heterogeneous catalystshave only recently been reliably and reproduciblysynthesized. These catalysts, therefore, have notyet received extensive attention but hold forth thetantalizing prospect of combining the advantagesof homogeneous and heterogeneous catalysts.

Computational catalysis efforts have historicallyfocused on the study of both homogeneous cata-lysts and single crystal catalysts (the latter structuresserve as simplified models for the surface geome-tries of large nanoparticles). Both classes of systemsare readily treated with existing computationalapproaches and are highly relevant to experimen-tally studied catalysts. Less attention has been paid,however, to the computational study of sub-nanometer clusters on support materials (primarily

48 S C I D A C R E V I E W W I N T E R 2 0 0 8 W W W . S C I D A C R E V I E W . O R G

Recent Trends in Computational

CATALYSIS

Researchers at ArgonneNational Laboratory aredeveloping efficientcatalytic processes andare tackling questions ofenergy production, usage,and security.

49S C I D A C R E V I E W W I N T E R 2 0 0 8 W W W . S C I D A C R E V I E W . O R G

Figure 1. Methanol reacting on a subnanometer platinum cluster (light gray spheres) supported on an alumina substrate. Aluminum atoms are darkgray, oxygen is red, carbon is yellow, and hydrogen is light blue.

because of the lack of suitable experimental datato which to compare the computational results) orto the explicit, electronic structure-based simula-tion of large, heterogeneous catalytic nanoparti-cles (due to the lack of suitable hardware andsoftware to perform the calculations).

Current Computational ResourcesComputer codes are providing new opportuni-ties for exploring and understanding the catalyticproperties of clusters and particles ranging froma single atom to nanoparticles of 3–6 nm in size.Most existing codes employ, in some form, theKohn–Sham formulation of density functionaltheory (DFT; sidebars “Quantum Chemical Meth-ods for Energy Predictions,” p51, and “DensityFunctional Theory,” p52). This approach providesa reasonably reliable and computationally feasi-ble methodology of calculating energies and bar-riers for reactions occurring on a variety ofcatalytic sites. In addition, DFT provides informa-tion on the electronic structures, wave functions,charge distributions, and spectroscopic charac-teristics of catalysts.

A variety of DFT-based codes is employed,including VASP, Dacapo, CASTEP, Car-Parrinello,CRYSTAL, Gaussian, Jaguar, and others. Each codeemploys different basis sets, boundary conditions,and density functionals, and each finds a niche inparticular types of catalytic problems. In somecases, molecular dynamics functionalities are alsoincorporated into the codes, either through ab ini-tio MD methods (for example, Car-Parrinello) orthrough approximate, reactive forcefields.

New computer hardware is also advancingcomputational catalysis. Numerous researchersemploy home-built Beowulf clusters, and high-performance computing clusters at DOE facili-ties—such as Environmental Molecular SciencesLaboratory (Pacific Northwest National Labora-tory), National Energy Research Scientific Com-puting Center (NERSC, at Lawrence BerkeleyNational Laboratory), and Blue Gene (ANL; OakRidge National Laboratory)—provide the neces-sary hardware to expand computational studies.Indeed, the dramatic rise in computing power,which will reach petaflop-scale in the next fewyears and exascale beyond that, will provide

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Computer codes areproviding newopportunities for exploringand understanding thecatalytic properties ofclusters and particles.New computer hardwareis also advancingcomputational catalysis.


researchers in the near future with the unprece-dented capabilities to study the catalytic proper-ties of clusters and nanoparticles.

Subnanometer Heterogeneous CatalysisMetal clusters of less than one nanometer indiameter have, until recently, been difficult to syn-thesize in a reliable and reproducible manner.Recent advances in synthetic techniques, how-ever, have begun to overcome these problems,and subnanometer clusters are emerging asobjects of intense interest in both the experimen-tal and computational catalysis communities.

The clusters are known to possess reactivityproperties not observed in their bulk analogs,which makes them attractive candidates for use

as novel catalytic materials. The distinct catalyticproperties of the clusters are often hypothesizedto result from their unique geometric and elec-tronic characteristics, such as the presence of ahigh density of highly under-coordinated surfaceatoms. These under-coordinated atoms, in turn,are expected to possess unique capabilities forbond breaking and bond formation.

Given their relatively small size, subnanometerclusters are readily treated with DFT-based com-putational approaches. Such approaches can pro-vide detailed information about the clustermorphologies and the reactivity properties ofindividual atomic features on the clusters. Thisinformation would be extremely difficult toobtain by purely experimental methods, and as


Catalysis is the study of materials that can controlchemical transformations. The ideal catalyst for a givenchemical reaction satisfies two general criteria:

l It converts the starting chemicals (reactants) to thedesired products with no production of undesiredbyproducts (in other words, it is perfectly selective)

l It enables the reaction to proceed at a very high rate (itis very active)

In practice, real catalysts must compromise on one orboth of these criteria, and the challenge of catalyst designis to find materials that will come as close to optimalperformance as possible.

Complex chemical reactions generally comprise manyinterrelated elementary reaction steps. Catalysts functionby altering the kinetics and thermodynamics of thevarious elementary steps. By lowering the activation

barrier for elementary steps that lead to desired products,for example, the catalyst can increase the rate offormation of those products (figure 2). Conversely, byraising the activation barrier for steps leading to undesiredproducts, the catalyst can suppress the formation ofthose unwanted chemicals. The catalyst is thus seen tobe a sort of master puppeteer, controlling the intricateinteractions between different chemical species andelementary reactions to produce desired chemicalproducts with a high degree of activity and selectivity.

Catalysts can take many forms. Among the forms mostactively investigated are homogeneous catalysts, whereinisolated metal atoms and associated ligands aresuspended in solution, and heterogeneous catalysts,wherein metal clusters or nanoparticles are immobilizedon support materials (figure 3). Both types of catalystsfind broad application in the fundamental sciences andindustry.

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Figure 2. Potential energy surface illustrating howcatalysts reduce the activation barriers of reactions.

Figure 3. A gold nanoparticle supported on an oxidesubstrate. Hydroxyl, carbon monoxide, and carbondioxide molecules are reacting on the surface of thenanoparticle.

The dramatic rise incomputing power, whichwill reach petaflop-scale inthe next few years andexascale beyond that, willprovide researchers in thenear future with theunprecedentedcapabilities to study thecatalytic properties ofclusters and nanoparticles.

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such, the calculations provide a powerful com-plement to the experimental studies. For exam-ple, bonds between carbon and hydrogen areextremely stable and difficult to break in chemi-cal reactions. However, recent computationalinvestigations of the reaction energies and bar-riers of alkanes adsorbed on metal atom clustershave found that certain very small clusters (con-taining only four to eight metal atoms) have verysmall barriers to breaking carbon–hydrogenbonds compared to much larger barriers for thebulk metals. The computations indicate this isbecause the under-coordinated atoms in the clus-

ters strongly attract electrons from the bond,which thereby weakens the bonds. These results,in turn, suggest subnanometer metal clusters maybe highly active for reactions involving small alka-nes and alcohols (figure 1, p49). Indeed, these pre-dictions are completely consistent with recentexperimental results that show a very high activ-ity of Pt8–10 clusters for carbon–hydrogen bondcleavage.

Electrocatalysis on Subnanometer ClustersNanometer- and subnanometer-sized clusters canalso show unique reactivity properties in

Can current electronic structure methods predictreaction energies and barriers of catalytic reactions tothe chemical accuracy? The long term goal of modernquantum chemistry has been the prediction ofthermochemical data to an accuracy of ±1 kcal/mol,which is key to the reliable prediction of structure,stability, and reaction mechanisms.

Hartree–Fock calculations used in the early years ofquantum chemical computations are based on themean field assumption and are surprisingly accuratefor many properties. However, large errors (up to 100kcal/mol) can be produced in bond energies whereelectron pairs are being broken. Such methods proved

to be unsatisfactory for such predictions of reactionenergies and barriers. Accounting for interactionbetween all electrons is very complicated, but toolssuch as coupled cluster methods from many-bodytheory has been very successful in accounting forcorrelations between electrons and providing accuratepredictions of thermochemical data to chemicalaccuracy. Methods such as Gaussian-4 theory areable to predict reaction energies and barriers accurateto ±1 kcal/mol. These methods are being used tovalidate the widely used density functional methods,which scale better and can handle much largersystems (figure 4).

Quantum Chemica l Methods fo r Ene rgy P red i c t i ons

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Figure 4. An electron density plot for the reaction of propane with a four-atom platinum cluster. The carbon andhydrogen atoms are gray and white, respectively, while the platinum atoms are darker.

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electrocatalytic environments (sidebar “Electro-catalysis”). For a critical reaction of interest in theanodes of fuel cells, carbon monoxide (CO) elec-tro-oxidation, these clusters can show substan-tially higher intrinsic activity than either smoothor stepped metal electrode surfaces. However, aswith the propane/platinum subnanometer clus-ters described above, the experimental resultsalone do not provide a molecular-level explana-tion for the remarkable properties the electrocat-alytic clusters display. To develop such anexplanation, computational catalytic studies canbe of great use.

DFT calculations were used to estimate theenergetics of the reactants and products associ-ated with the key elementary steps in this reac-tion network. Estimates of the relative rates of COelectro-oxidation on various geometrical featureswere obtained by performing these calculationson subnanometer clusters supported by metalsubstrates, single crystal models of electrodesteps, and perfect single crystal terraces. More sig-nificantly, molecular-level explanations for thedifferences in activity were derived.

CO functions as both a reactant and a poisonfor this reaction. If the CO is bound too stronglyto the surface, it will impede the adsorption ofOH (the other key reactant) and will inhibit fur-ther reaction. The calculations indicate multipleCO molecules can adsorb on highly under-coor-dinated atoms on the subnanometer clusters. Thishigh coverage of CO effectively repels neighbor-ing CO molecules and makes them easier toremove from the surface, thus reducing the poi-soning effect. On the other hand, the calculationsalso show hydroxyl (OH) groups, a key interme-diate in the reaction, are significantly stabilizedon the highly under-coordinated adsorption sitespresent on the subnanometer clusters, and thegeometry of the adsorbed OH is such that it caninteract naturally with surrounding water mole-cules and be stabilized by the associated hydro-gen bonding (figure 5). The net result of these twoeffects is higher CO electro-oxidation activity onthe clusters than on steps, terraces, or other sur-face features.

Heterogeneous Catalysis:Single Crystals and Large NanoparticlesAlthough the scientific possibilities associatedwith subnanometer heterogeneous catalysis arevery promising, heterogeneous catalysts employ-ing larger nanoparticles remain a crucial andindispensable component of this field. Recentcomputational efforts in this area have been veryfruitful and have added an important new dimen-sion to the understanding of heterogeneous catal-ysis on nanoparticles.


Figure 5. Reaction of carbon monoxide and hydroxyl groups on Pt(111)-supportedplatinum adatoms. The adatom is shaded dark gray, and the other platinum atoms arelight gray. Oxygen is red, carbon is black, and hydrogen is white.

Electrocatalysis is the study of catalysis in electrochemical environments. Suchenvironments are typically aqueous, can be extremely complex, and may involve a varietyof phenomena that play a lesser role in many other types of catalysis, including solid—liquid interfacial effects, solvated charges, charge transfer, and strong electric fields. Theactivity and selectivity of electrocatalysts can be tuned by changing the catalyst potential;this provides a degree of control over the catalytic properties that is not always available inother types of catalysts. Electrocatalysts can, however, suffer from significant stabilityproblems due to enhanced metal dissolution and corrosion that occur in electrochemicalsystems, thereby significantly restricting the types of materials that can serve as robustelectrocatalysts.

E l e c t r o c a t a l y s i s

The conventional approach to the calculation of the bonding of atoms has involved aquantum mechanical solution for the electron wave function. An alternative approachis a description based on the density of electrons at any point in space, which isreferred to as density functional theory (DFT). This simpler method is much lessexpensive computationally than the wave function approach.

In principle, DFT provides an exact path to the determination of the electronicenergy of a system in a ground state. In practice, the functional form to do this is notknown, but in recent years great progress has been made in deriving functionals thatgive a reasonably accurate description of molecular and solid systems. The dramaticincrease in computer resources currently available has made it possible to computereaction energies and barriers of many complex catalytic systems that can be used indesigning new catalysts. Functionals such as RPBE, B3LYP, and PW91 are amongthe most popular for studies of catalytic reaction mechanisms.

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In particular, periodic DFT codes have permit-ted the accurate calculation of binding energies,activation barriers, and other key catalyticparameters on a large number of single crystalmetal and oxide surfaces. These approaches havebeen instrumental in elucidating the atomic-scale chemistry and physics that govern themacroscopic properties of single crystal hetero-geneous catalysts. Further, by supplementingthese calculations with thermodynamic tech-niques and the Wulff construction (sidebar“Wulff Construction”), it has been possible toextrapolate the behavior of large nanoparticlesfrom the calculated behavior of single crystalfacets.

Once catalytic properties have been calculatedon various single crystal facets of a given metal oralloy, the Wulff construction can be used to effec-tively combine these results to build a picture ofthe catalytic properties of more complexnanoparticles, including the effect of catalyst sup-ports on the reaction chemistry (sidebar “Cata-lyst Supports,” p54). In addition, for smallerparticles, energy contributions of edges and evencorners become non-negligible and introduce adependence of particle shape on the particle size.Therefore, such approaches can ultimately yield,for example, ab initio predictions of the catalyticproperties of metal nanoparticles as a function ofthe nanoparticles’ size. A recent example of sucha prediction, in this case for the oxygen reductionreaction, is shown in figure 7 (p54).

Heterogeneous Catalysis—ElectrocatalysisIn addition to their longstanding applications totraditional heterogeneous catalysis, highly paral-lelized electronic structure calculations are beingincreasingly applied to the study of complex elec-trocatalytic phenomena. While traditional elec-tronic structure/DFT calculations are notnaturally applicable to problems of constant elec-trode potential, as is found in electrochemistry,various techniques have been recently developedto straightforwardly extrapolate electrochemicalenvironments from standard electronic structurecalculations. One particularly simple approach isbased on the concept of the “theoretical standardhydrogen electrode.” This scheme is used to deter-mine the potential dependence of the free ener-gies of elementary reaction steps involvingtransfer of protons and electrons. The potentialenergy change of proton/electron transfer reac-tions of the type

A→B+H++e-

is evaluated by replacing the proton/electron pairwith half of an H2 molecule; the energies are thenstraightforwardly calculated with standard DFTor other electronic structure techniques (to esti-mate the effect of the aqueous environment onthese values, water molecules may be explicitlyadded to the simulation cell). Simple entropy cor-rections are then added to get the free energychange of the reaction. The resulting free energy


In the general case, the equilibrium shape ofcrystals is anisotropic. It can be found from therequirement, going back to Gibbs, that the totalsurface free energy of a crystal with fixedvolume V has to be at minimum:

∫γdA = min

where γ is the surface energy and A is thesurface area. The shape can be determined withhelp of the Wulff theorem, formulated in 1901,which states that the minimum surface energycondition is equivalent to γi/hi= constant, whereγi is a surface free energy of a crystal face i andhi is the distance from the center to the face i.Here, the surface free energy is defined as halfthe energy per unit area required to separatethe crystal parallel to the plane i. The Wulffconstruction gives the crystal shape as the innerpolyhedron formed by planes orthogonal to

lines drawn from the origin and intersectingthese lines at distances proportional to thesurface energy of the respective faces. Thus,knowledge of surface energies for crystal faceswith Miller indices (hkl) provides knowledge ofcrystal shape. First-principles calculations withperiodic boundary conditions are capable ofproducing accurate surface and interfacialenergies. The surface energy also depends onthe composition and coverage of adsorbedspecies, and first-principles studies providetheir binding energies. Thermodynamictreatments and the Wulff theorem permit us touse these energies to predict equilibriumparticle shapes and surface chemistries indifferent environments. An example of suchprediction of shape of a platinum particleannealed in argon and argon/oxygen mixture isshown in figure 6.

Wu l f f C o n s t r u c t i o n

Figure 6. Shape of a catalytic platinumnanoparticle, as determined with a combinationof first-principles calculations and classicalthermodynamic optimizations (Wulffconstructions).

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change corresponds to the situation of zeropotential on the standard hydrogen electrodescale. To determine how the free energy changeswith potential, the potential multiplied by thenumber of transferred proton/electron pairs issimply subtracted.

The strategy described above is quite power-ful in its scope, as it rapidly deploys to electro-chemical problems the theoretical andcomputational approaches that have been devel-oped for traditional heterogeneous catalysis overthe past 15 years. Computational approaches can,thus, be used to provide molecular-level insightinto catalytic chemistry on metals and othermaterials in electrochemical environments inmuch the same way such insight has beenobtained in traditional catalytic systems.

For example, the oxygen reduction reaction, anelectrochemical reaction essential for the opera-tion of low-temperature fuel cells, has recentlybeen studied using DFT calculations on singlecrystal surfaces. By calculating the free energieschanges for all elementary steps associated withthis reaction on metal surfaces for a large num-ber of different elements, a classic volcano plot(sidebar “Volcano Plots”) was developed for thereaction. The predictions of the relative catalyticactivities of different platinum alloys determinedfrom this volcano plot were subsequently shownto match extremely well with careful experimen-tal measurements. This close match between pre-dicted rates from computational electrocatalytictheory and experimental results suggests, in turn,that computational techniques might be well-suited to screening and design of new alloys forapplications in catalysis.

Screening TechniquesElectronic structure-based computational tech-niques are being increasingly applied to the pre-diction of novel catalyst properties. The ultimategoal of these predictive efforts is to screen for anddesign new catalysts for desired reactions of inter-est. These types of computationally based eval-uations can generally be done at a fraction of thecost of analogous experimental screening.

Computational catalyst screening is completelydependent upon modern, high-performancecomputers. For a screening effort to be successful,it is typically necessary to evaluate the catalyticproperties of hundreds of transition metal alloys,oxides, or other catalytic materials. Such evalua-tions can only be performed if reliable access tohigh-performance computing systems, such asthe new 100 TF Blue Gene/P at the Argonne Lead-ership Computer Facility, are available.

The basic computational screening procedureis straightforward. First, simple descriptors


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Figure 7. Calculated reaction rate (TOF) of the Oxygen Reduction Reaction (ORR) onplatinum and gold nanoparticles as a function of particle size. The rates are normalizedto the calculated rate on a Pt(111) single crystal surface.

Supports provide a platform from which heterogeneous catalysts, such as ananoparticle, can act to change the rate of a reaction without being consumedduring the reaction. The support material may or may not take part in the catalyticreaction. The support is usually a surface such as a metal oxide or carbon material.The support and catalyst may bond together in such a way to enhance the reactivityof the catalyst; in other cases, the support may be inactive and provide a highsurface area substrate to increase the collisions of the reactants with the catalysts.For example, in catalytic converters, a ceramic honeycomb acts as a high surfacearea support for the catalyst such as platinum, rhodium, or palladium for changingpollution gases from the engine to environmentally friendly products. In fuel cells,platinum catalysts are located on a carbon support, which provides a means forconduction of the electrons for the electrocatalytic reactions.

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(catalytic parameters that can be straightfor-wardly determined with DFT or other electronic-structure methods) are identified for the reactionsor processes of interest. These descriptors should,with the aid of simple thermodynamic or kineticanalyses, including the famous volcano plot (side-bar “Volcano Plots”), be able to predict trends inthe activity and selectivity of catalysts with a rea-sonable degree of accuracy.

The value of these descriptors is then deter-mined on a large search space of possible materi-als. DFT simulations and correlations betweenvariables in pre-existing computational databasesare used to calculate these descriptor values. Themost interesting materials for the reaction orprocess of interest (that is, the materials withdescriptor values that lead to optimal properties)are then identified. The stability and durability ofthese materials in practical reactive environmentstoward surface segregation, islanding, and disso-lution are then assessed. Finally, the most prom-ising catalysts are tested experimentally. This

approach can be thought of as a “catalyst filter-ing” process (sidebar “Materials Filtering,” p56),and the end result is the identification of a limitedpool of candidate catalysts that are predicted tohave excellent properties.

This approach has been used successfully to iden-tify improved catalysts for a limited number of reac-tions in heterogeneous catalysis and electrocatalysis.Recently, more than 700 bimetallic alloys were eval-uated with a computational catalytic screening pro-cedure as candidates for hydrogen evolutioncatalysts (a reaction of interest in the production ofhydrogen from acids and in reversible, low-temper-ature fuel cells). From this large pool of candidatealloys, a few were identified as suitable for experi-mental testing, and one such alloy was subsequentlyshown to have superior experimental performanceto pure platinum, the canonical catalyst used in thisreaction (figure 10, p57).

Although computational catalytic designefforts have already been proven to be successful,these efforts could be enhanced by generating and

Many heterogeneously catalyzed reactions are highlysensitive to the particular metal, oxide, or other materialthat is employed as a catalyst. These reactions showpronounced changes in both activity and selectivity fromone catalyst to the next, and to select the most usefulcatalysts for the reaction of interest, it is essential tounderstand these changes.

Trends in activity from one catalyst to the next oftentake the form of volcano plots (figure 8). In these plots, aparticular descriptor of the catalytic activity for the reactionof interest (for example, the activation energy of a

kinetically important elementary step) is determined foreach catalyst and forms one axis of the plot; the other axisis typically the catalytic activity at a fixed set of conditions.As the name suggests, these plots often take the form of avolcano with a clearly identifiable maximum in the activity.The location of the maximum often signifies a change inthe most kinetically important (rate limiting) step of thereaction and is also related to the well-known SabatierPrinciple, which states that optimal catalysts will interactneither too strongly nor too weakly with the reactant andproduct molecules.

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Figure 8. Calculated volcano plot for the ORR. The y-axis shows the calculated activity for each metal (the naturallogarithm of the reaction rate) while the x-axis shows the calculated binding energy of atomic oxygen on the samemetals.

The ultimate goal of thesepredictive efforts is toscreen for and design newcatalysts for desiredreactions of interest.These types ofcomputationally basedevaluations can generallybe done at a fraction ofthe cost of analogousexperimental screening.

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organizing large databases of physical and chem-ical property data. A “materials design work-bench” could be created based on a repository ofdata generated from accurate electronic structurecalculations. Using a mix of artificial intelligenceand computer science methods, the workbenchwould guide users to identify candidate materi-als—and perhaps, by extrapolation, aid in theproposal of novel candidates that may not havebeen computationally analyzed.

The application of the concept of computationaldesign to new subnanometer catalytic materialswill also be very fruitful. The clusters and nanopar-ticles have a wide range of compositions but are ofsmall enough size so that current and near-futurecomputer capabilities should be adequate for mak-ing significant progress in materials by design.

Scaling to Larger SystemsToday’s electronic structure methodologies arewell-suited to treating systems with a limited num-ber of atoms and electrons. However, the compu-tational cost of electronic structure calculationsincreases rapidly with system size, which thenrequires prohibitively long run times. One solutionto this problem is, in principle, quite straightfor-ward: simply run the calculations on more proces-sors. However, most electronic structure codesgenerally do not parallelize well beyond O(100)processors, and fundamentally new codes andalgorithms are needed to take full advantage of theprocessing power of petaflop-scale machines.

Efforts are currently under way to develop DFT-based electronic structure codes capable of par-allelizing efficiently out to thousands of


The goal of virtual catalyst design requires that a variety ofdesign criteria be taken into account, including the activityof the catalyst for the reaction of interest, the selectivity,and the stability; each of these general criteria, in turn, mayinvolve the evaluation of a variety of descriptors or theassessment of multiple processes that could destabilize thecatalyst. For conceptual purposes, each of these processesor criteria could be thought of in terms of a filter, and thedesign work as a series of filtrations (figure 9). The work

begins with a large pool of potential catalyst candidates.This initial group is passed through successivecomputational filters, which eliminate the catalysts that arenot predicted to be active for the reaction of interest. Nextto be eliminated are those catalysts that produce too muchof one or more byproducts. Finally, catalysts susceptible tovarious types of destabilization events are eliminated, untila much smaller pool of catalysts—ones likely to be active,stable, and selective—has been identified.

M a t e r i a l s F i l t e r i n g

Activity Screening Filter

Selectivity Filters

Stability Filters

Hundreds of Alloys

Figure 9. Schematic of the computatational catalyst filtering process. An initial pool of catalytic candidates issequentially tested for activity, selectivity, and stability properties, and unsuitable catalysts are filtered out.

Using a mix of artificialintelligence and computerscience methods, theworkbench would guideusers to identify candidatematerials—and perhaps,by extrapolation, aid inthe proposal of novelcandidates that may nothave been computationallyanalyzed.

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processors. Such codes could perform realisticcatalytic simulations on nanoparticles with atleast 1,000 metal atoms. Entirely new possibili-ties may then be revealed for both the computa-tional understanding of heterogeneous catalysisand the design of novel catalytic nanoparticles.

Gpaw is a new real-space, projector augmentedwave-based DFT code being developed to over-come the limitations described above. Initiallydeveloped in Professor Jens Nørskov’s group atthe Technical University of Denmark, the coderepresents a very efficient implementation ofDFT. The code’s projector augmented wave for-malism represents one of the most efficient extantapproaches for treating systems with large num-bers of core electrons. In addition, the real-spacealgorithm at the heart of the code is inherentlymore parallelizable than DFT codes that employbasis functions, and encouraging preliminaryresults about Gpaw’s scalability to large numbersof processors have already been obtained. A col-laboration between the Technical University andseveral Argonne divisions has further optimizedthe code to make it suitable for use on systems ofBlue Gene scale. Successful completion of this

work should yield a code that can efficiently andaccurately treat catalytic particles up to 3–4 nmin diameter and will represent a breakthrough incomputational catalytic science.

SummaryThe tremendous progress in recent years on com-putational catalysis results from the advancedcomputational hardware at numerous supercom-puting centers around the world. This researchprovides important insights into the physical andchemical phenomena that underlie homoge-neous and heterogeneous catalysis. This progresscontinues, and new efforts to extend traditionalsingle crystal catalytic modeling to subnanome-ter metal clusters, electrochemical systems, andvery large—O(1,000) atoms—metal nanoparti-cles, together with new initiatives in computa-tional catalytic screening and design, are underway. The combination of such diverse areas ofinquiry within a single field should ensure itsgrowth and relevance for many years to come. l

Contributors: Jeff Greeley, Peter Zapol, and Larry Curtiss,Argonne National Laboratory


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Figure 10. Computational combinatorial screening results for the Hydrogen Evolution Reaction (HER). Each circle represents a binary surface alloy.The color of the circle denotes the free energy of hydrogen adsorption on the alloy, ∆GH, as calculated by DFT. Lighter colors indicate higherpredicted catalytic activity for the HER.

The tremendous progressin recent years oncomputational catalysisresults from theadvanced computationalhardware at numeroussupercomputing centersaround the world.

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recent trends in computational catalysis

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