The Materials Super Highway: Integrating
High-Throughput Experimentation into
Mapping the Catalysis Materials Genome
Jason Hattrick-Simpers, Cun Wen &Jochen Lauterbach

Catalysis Letters
ISSN 1011-372X
Volume 145
Number 1
Catal Lett (2015) 145:290-298
DOI 10.1007/s10562-014-1442-y

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Author's personal copy
Catal Lett (2015) 145:290–298
DOI 10.1007/s10562-014-1442-y

The Materials Super Highway: Integrating High-Throughput
Experimentation into Mapping the Catalysis Materials Genome
Jason Hattrick-Simpers • Cun Wen •Jochen Lauterbach

Received: 12 October 2014 / Accepted: 13 November 2014 / Published online: 27 November 2014
Ó Springer Science+Business Media New York 2014

Abstract The materials genome initiative (MGI) aims toaccelerate the process of materials discovery and reduce
the time to commercialization of advanced materials. Thusfar, the MGI has resulted in significant progress in computational simulation, modeling, and predictions of catalytic materials. However, prodigious amounts ofexperimental data are necessary to inform and validate
these computational models. High-throughput (HT) methodologies, in which hundreds of materials are rapidly
synthesized, processed, and characterized for their figure of
merit, represent the key experimental enabler of the theoretical aspects of the MGI. HT methodologies have beenused since the early 1980s for identifying novel catalyst
formulations and optimizing existing catalysts. Many
sophisticated screening and data mining techniques havebeen developed and since the mid-1990s, this approach has
become a widely accepted industrial practice. This article
will provide a short history of major developments in HT
and will discuss screening approaches combining rapid,
qualitative primary screens via thin-film techniques with aseries of quantitative screens using plug flow reactors. An
illustrative example will be provided of one such study inwhich novel fuel-flexible sulfur tolerant cracking catalysts
were developed. We will then illustrate a path forward thatleverages existing HT expertise to validate, provide
empirical data to and help guide future theoretical studies.
Keywords Combinatorial chemistry  Methodology andphenomena  Nanotechnology  High throughput

J. Hattrick-Simpers  C. Wen  J. Lauterbach (&)
Department of Chemical Engineering, Smartstate Center
for Strategic Approaches to the Generation of Electricity,
University of South Carolina, Columbia, SC 29209, USAe-mail: lauteraj@cec.sc.edu

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1 Introduction
Two major goals of the materials genome initiative (MGI)
are to cut the time between discovery and commercialization of advanced materials in half, from 10–20 to
5–10 years, and to do it at lower cost [1]. This is accomplished by creating an integrated design continuum thatleverages computational, experimental and open-data
informatics tools, expediting the discovery of new scienceand breaking down obstacles to commercialization (Fig. 1).
The ultimate goal of the MGI is to educate a next generation workforce capable of utilizing these tools sets topromote national security, clean energy, and human welfare. Thus far, the MGI has made significant progress incomputational simulation, modeling, and predictions ofmaterials properties [2, 3]. In the fields of catalysis,
examples of the successful use of theory include the designof alloy catalysts based on DFT calculations for hydrocarbon steam reforming and acetylene hydrogenation [4–6]. However, though theory often can inform experiments,
a feedback route with experimentation is necessary forvalidation and new hypothesis generation. As an example,
a recently reported Ni–Ga based catalyst is active for CO2
reduction to methanol, however it resides in a complicatedportion of the phase diagram where competition between
the d and c phases exists. [7]. It is therefore likely that onstream the surface of the catalyst is quite different fromthat of the theoretically predicted active phase, but without
experimental data characterizing these changes during
operation new innovation is stymied. In addition, it is wellknown that theoretical studies still struggle to make accurate predictions when working in complicated gaseousenvironments or accounting for the effect of point defects,
ion transport and interfaces in determining the reaction
rates and selectivities [8–10]. Despite these well-known

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The Materials Super Highway

Fig. 1 MGI overview illustrating the overlap between the three core
fields and the breadth of its impact on technology and the workforce[1]

caveats to theoretical studies, efforts in the MGI havelargely focused on theory, disregarding the important roleof experimentation.
As theory becomes more routinely applied to catalysis
problems, prodigious amounts of experimental data are
required to inform and validate these computational models, thus powering the MGI computational ‘‘engine.’’Modeling studies rely on accurate depictions of materials
properties including crystal structure, composition, andsurface coverage/arrangements under catalytic reaction
conditions [11]. The latter property is of paramountimportance, as it is often the case that the surface of acatalyst under reaction conditions (and thus the very natureof its active sites) does not resemble the equilibrium surface at ambient conditions, let alone at 0 K [12]. Moreover,
the role of interfaces, in particular those between the catalyst and the support, have only recently begun to bemodeled theoretically [13, 14]. When one considers,
however, that these interfaces are likely as dynamic as thecatalyst surface, it becomes clear that without experimental
validation of the role these important parameters play incatalysis and how they change during reactions a speed
bump in the ‘‘Materials Super Highway’’ is imminent.
High-throughput experimental (HTE) methodologies,
which allow accelerated synthesis and testing of materials
for optimized performance, are uniquely suited to rapidly
generate high quality experimental data, and hence represent the key enabling technologies to bring the computational materials design efforts of the MGI to fruition. The
field of catalysis was an early adopter of high-throughput
screening technologies; the original Haber–Bosch catalyst
was discovered in 1913 by systematically studying over291

2,500 potential catalysts with 6,500 experiments before
settling on iron as the catalyst [15]. Today, the Haber–
Bosch process is responsible for synthesizing the world’s
ammonia supply, mainly used for fertilizer production,
which makes it possible for our globe to sustain more than
7 billion people [16]. The advent of powerful personal
computers capable of automated data acquisition andanalysis in the late 1980s, coupled with the marked successof pharmaceutical companies in novel drug discovery via
parallelized synthesis and screening, led to a resurgence ofinterest in the in the 1990s. There are several outstanding
recent reviews on the topic of high-throughput materials
science; in particular for the field of catalysis [17]. Theinterested reader is particularly encouraged to consult
recent reviews by Maier et al. and Green et al. [18, 19]. A
schematic of the overall high-throughput approach isshown in Fig. 2, where hypothesis generation is followedby the rapid synthesis of hundreds of different catalyst
formulations. The catalysts are then screened using either
parallel or sequential screening techniques, and specialized
data minimization/analysis software are then used to
extract information from the dense data sets.
Early adopters of high-throughput technologies in catalysis
included start-up companies, such as Symyx, Avantium and
HTE, large chemical companies, such as DOW, BASF, andExxonMobil, as well as a (small) number of academic labs.
While these first studies were important demonstrations of thepotential of high-throughput in catalysis, frequently they were
plagued by irreproducible results and overly aggressive patenting of large swaths of the periodic table. Early studies often
prioritized throughput in terms of synthesis and rapid reaction
screening over ensuring that catalysts were created and tested
in a manner consistent with the actual large-scale application.
Smotkin et al. reported, for instance, that the blanket use of
NaBH4 as a reductant during production of fuel cell catalysts
arrays rendered such an approach susceptible to local maxima
that are largely a function of synthesis method, and are notreflective of the ultimate material figure of merit [20]. Early
studies often characterized samples deposited on planar
samples using scanning probe mass-spectrometry, IR thermography, or optical imaging techniques, such as fluorescence [21–23]. Many of these techniques inevitably sufferfrom sample cross-talk since there is no barrier preventing
reactant and product spill-over between catalyst sites. There
have been recent studies that sought to minimize cross-talk inplanar samples by introducing physical barriers such as capillaries between individual catalysts [24].This approach also
removes a means of evaluating the catalyst-support interaction and the role of nanoparticle properties in determining
activity and selectivity [13]. IR thermography has the additional caveat of not being capable of product speciation, butrather only identifying ‘‘active’’ catalysts by monitoring theirheat signatures.

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J. Hattrick-Simpers et al.

Fig. 2 Schematic
representation of the highthroughput experimental
cyclical workflow. Work is
initiated through the generationof a set of hypotheses, rapid
synthesis techniques are thenused to design an array ofcatalytic materials. Parallel
screening techniques are used toscreen the catalysts for theirproperties, which are used toquantify figure of merit using ahost of data minimization andanalysis tools

Modern HT catalysis studies are often multi-facetedwith multiple stages that first rapidly identify potential hits
using qualitative measurements and subsequently hone inon materials of interest in increasingly greater detail underindustrially important conditions [25–27]. Each study
begins with an analytical search of the relevant technicalliterature to identify important parameters for a particularcatalytic reaction. An example of such a heuristic would be
the importance of the ratio of zeolite pore size to feed stock
and product molecular sizes in biofuel platform chemical
upgrading [28]. Once a suitable heuristic is identified, arapid primary screen is then undertaken that uses either
robotic pipetting systems or thin film depositions to create
arrays of hundreds to thousands of samples on a single
chip. These arrays are then screened via a qualitative primary screen, e.g., using optical techniques or thermography to identify initial leads, such as a coke inhibiting
additive [29]. This is quite similar to the approaches used
above with the same caveats, however, the goal is toquickly identify potential winning/losing regions and thenmove the most promising regions of the parameter space
directly to bench-scale reactor studies. At the second stageof the screen, milligram quantities of the catalyst are produced via a host of traditional methods, which can beparallelized and automated. Such methods include wet
impregnation, reverse micelles, parallel hydrothermal
synthesis, etc. including impregnation onto suitable structured supports. Quantitative screening is generally undertaken via parallel reactor studies, where cross-talk between
catalysts is prevented and effluent streams are analyzed
separately, often via serial GC/MS or imaging FTIR [30–33]. Final screening of select catalysts can be done in123parallel pilot scale reactors, generally single-bed reactors
although commercial multi-bed reactors are available, withcatalysts prepared in kilogram quantities and run under trueindustrial conditions.
In addition to the catalytic performance of the synthesized materials, a great deal of effort is devoted toobtaining fundamental understanding of the structure andsurface composition of promising new catalysts. This
information is just as vital as catalytic performance for
guiding theoretical MGI studies as the baseline necessaryfor DFT studies is the bulk crystal structure and possibly
the surface configuration. To this end, a number of truly
HT structural characterization techniques are routinely
applied including scanning synchrotron X-ray diffraction(bulk structure) [34–36], X-ray absorption spectroscopy(local coordination) [37], and X-ray photoelectron spectroscopy (surface composition and element coordination)[38]. A variety of compositional tools, such as atomic
adsorption spectroscopy, inductively couple plasma analysis, and SEM/EDS are readily parallelized and are utilized regularly in HT studies [39, 40]. Conversely,
measurements that provide detailed information about the
microstructure of nanoparticles such as high resolution
TEM are inherently slow and time consuming, and are used
only for ultimate material validation [41]. This despite thefact that a truly integrated theoretical–experimental workflow may benefit from the early insights provided to eachother as hits and misses are identified and related between
practitioners.
Integration of high-throughput experimental and fundamental modeling efforts for catalyst design have been
discussed for more than 10 years [33, 42]. Figure 3

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illustrates an example workflow in catalysis where experiment and theory are used to drive both novel materials
discovery and create fundamental understanding of catalyst
performance. However, despite the progress of the HT
catalysis field, great opportunities exist to more fully
integrate its efforts into the worldwide MGI efforts. In thenext section, we will provide a case study of a successful
HT experimental project in academia, where a new class ofcatalysts were hypothesized, discovered, and optimized inless than 10 months. We will illustrate opportunities where
theory and data science could have provided clear direction
and further accelerated catalyst development, while
extracting new knowledge that would have benefited work
on dissimilar catalytic processes with similar materials. We
will then generalize these opportunities and provide a viewof what the next step down the ‘‘Materials Super Highway’’
could look like for the field of catalysis. Finally, we willshow how these opportunities can be consolidated with ageneralized High-throughput Experimental Center whereexperts in the art of HT catalysis will join with those
applying HT to electronic materials, structural materials,
and energy materials towards creating experimental anddata mining tools benefiting all groups mutually.

2 Case Study for JP-8
We recently applied HT techniques to develop catalysts foron-site production of LPG from diesel and JP-8 to power
portable energy sources [27]. Portable energy sources arevital to a number of applications where grid power is difficult to obtain, such as cell towers in remote locations andmilitary operations in forward operating bases. The recent
advent of solid oxide fuel cells capable of operating onliquefied petroleum gas (LPG) provides an avenue towards
better overall energy efficiency over the traditional diesel
fuel generators currently in use. However, LPG supply
lines do not exist in rural locations, and in disaster response
situations it would work against humanitarian aims to293divert critical transportation resources from carrying
foodstuffs and medical supplies to transporting LPG. This
need motivated our group to develop a fuel flexible catalyst
that can convert diesel, gasoline, or JP-8, readily available
fuels in such locations with established supply lines, to
LPG on-site.
Catalytic cracking is the most promising and economic
process to generate LPG from diesel or JP-8. However,during direct catalytic cracking of heavy hydrocarbons athigh conversion, catalyst deactivation via coke formation isinevitable [27]. Thus, one must maintain the rate ofcracking while promoting air-only burn-off of the coke via
additives. Additionally, although modern diesel fuels havebeen de-sulfurized substantially (15 ppm) to meet EPA
requirements, diesel in other parts of this world may contain much higher levels of sulfur and JP-8 can contain asmuch as 3,000 ppm of sulfur. Sulfur deactivation of catalysts is well-known in the field [43], and often is an irreversible process, thus catalysts that can ‘‘select out’’ sulfur
containing molecules, and thus prevent deactivation, arerequired.
We developed a multi-tiered high-throughput approachcombining qualitative primary screens using thin-film
samples with quantitative powder screening using mg
powder quantities to rapidly discover a catalyst formulation
with optimized properties. The total time from project start
to final catalyst selection was *10 months. This project
would have never been successful using traditional single
sample at a time methodologies. The freedom of HTafforded our group to branch out from ‘‘safe’’ materials
such as normal transition metal oxides to more high-riskhigh-reward systems such as zeolites. The screening process was broken down into three phases. First, initial
identification of a base catalyst formulation with suitable
conversion and selectivity was performed on powder catalyst samples using a 16-well high-throughput reactor,
shown in Fig. 4 [27]. This broad initial screen vetted morethan 50 solid-acid catalysts, including a series of transition
metal oxides and zeolites, in less than 3 months. Small

Fig. 3 An example of aworkflow encompassing HT
experimentation andfundamental modeling to
expedite the rate of catalyst
development. Data flows
through the vertical arrows
between the two thrusts via
searchable databases

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pore MFI-type zeolites proved to have the highest overall
conversion at the lowest temperatures although they were
initially thought to be unlikely to produce sufficient yields
due to sensitivity to sulfur poisoning. This is a tremendous
example of HT experimentation allowing scientists to try
outside of the box materials, leading to breakthroughdiscoveries.
Thin film composition spread studies of bimetallics werethen undertaken to identify potential anti-coking additives[29]. Here, nanoparticles of the additives were deposited
onto pressed zeolite pellets to simulate nanoparticles generated via wet impregnation and then images of the pellet
surfaces during JP-8/air exposure were recorded. Figure 5
shows an example of a series of images of taken on a thin film
sample as a function of time during JP-8 dosing. A series offour bimetallic systems were screened. The bimetallic Pt–Gd
system exhibited the overall slowest coking rate, the lowest
coke burn-off temperature, and was recommended for final
powder verification. A Pt–Gd modified zeolite demonstrated
superior time on stream performance with practically no
decrease in yield over multiple cracking/regeneration cycles
during final validation. Figure 6 summarizes the conversion
results for catalysts studied over the course of this investigation. The best catalyst is being commercialized in a
packaged plug-and-play fuel reformer targeted towards
military and commercial applications [44].
During this study there were several opportunities wheretimely intervention by either theory or data science could

J. Hattrick-Simpers et al.

Fig. 5 Images of Pt–Gd/ZSM-5 during JP-8 cracking. Darkening on
the sample corresponds to the coke deposition of coke on the catalyst
surface [29]have further reduced the time required to identify optimal
catalyst compositions, and possibly further increased theoverall figure of merit, as shown in Fig. 3. Theoreticalunderstanding of the role of the transition and rare earth
metals in the zeolitic framework, in particular their role inmitigating coking, could have provided guidance towards
other and better alternative additives. Illumination of therole of zeolite structure and Si/Al ratio on cracking of long
chain hydrocarbons might have provided additionalrational design criteria for zeolite selection. Likewise, astructured database illustrating secondary catalytic properties of importance to the project such as coking resistanceFig. 4 High throughput reaction system for JP-8 cracking catalyst screening with 16 parallel reactors. End products from each reactor are
analyzed separately by on-line GC–MS

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Fig. 6 An overview of the catalytic performance of more than 100
catalysts tested in a 9 month period illustrating the progression fromlowest to highest yield and its correlation with catalyst structureof transition metals, coking rates of zeolites, and their use
in direct cracking of even simple hydrocarbon mixtures
would have been of tremendous use. In fact many of thesesecondary characteristics of interest for the catalyst eventually selected have been investigated previously but theresults are scattered throughout the technical literature in
myriad papers, extended abstracts, and buried in patents orworse, unreported due to perceived failure. A coalescedand searchable version of this technical literature would
have greatly accelerated hypothesis creation and materials
development.

3 Catalyzing the Next Stage in the Materials Super
Highway
To this moment, in the field of HT catalysis approaches
that synthesize and screen catalysts using scalable techniques have been used only by a limited portion of thecommunity. Many synthesis techniques are capable ofproducing sufficient quantities of catalyst for a bench-scale
reactor, but would be commercially prohibitive, owing toexcessive solvent or reductant usage or the use of expensive precursors. Also to meet the commercialization aspirations of the MGI, catalysts should not be screened for an
abstract figure of merit under semi-realistic conditions.
Instead it would be beneficial to embrace the full complexity of catalytic systems with synthesis techniques,
inexpensive and commercially viable supports and reaction
conditions (pressures, temperature, gas-hour space velocities) comparable to those used at industrial-scale. Likewise,
if the purpose of catalyst development is to create catalysts
that can be used to manufacture useful chemicals at
industrial scales the use of surrogate feedstocks could leadto unexpected roadblocks at the development stage arising
due to exposure to more complicated feedstocks. Even

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fundamental HT studies could benefit from running in
more realistic conditions, although this would necessitate
more complex data analytics to extract mechanistic
understanding.
Future HT-MGI studies could be substantially empowered if an emphasis were to be placed on understanding thelinkages between theoretical identification of a new catalyst and its actual experimental synthesis. This will require
the creation of multi-scale modeling tools that can becoupled with in situ experimental tools for monitoring
catalyst composition, size, morphology, and elemental
coordination to create new catalyst design rules. It is analmost trivial matter to build a statistical design based on
design of experiments and use that to identify a condition
that will synthesize the desired materials, however, turningthat into transferable knowledge that can be generalized to
new systems is a great challenge yet to be addressed.
Even once a material can be produced after theoretical
identification, if its catalytic properties and structure are
not well understood then the theory-experimental cycle is
not closed. The current state-of-the-art in HT experimental
catalysis techniques are largely results driven, this is particularly the case for parallel reactor studies, and thus
provide limited insight into why one material or material
synthesis procedure produces markedly different activity
and selectivity. The current standard in the HT community
is to perform detailed structural investigations ex situ or atsubstantially reduced pressure/temperature/concentrationof the catalyst to try to extract information regarding how
the catalyst ‘‘reacts’’ to its environment. It is well understood in the field, however, that catalyst active sites can begenerated or changed under reaction conditions [45, 46].
In terms of the HT structural characterization of catalysts, great advances in the state-of-the-art are on thehorizon, which will permit ever greater amounts of detailed
information to be generated rapidly. For instance, techniques for HT screening of nanoparticle morphology andsize are being developed by parallel efforts such as Scalable Nano-manufacturing, which will greatly accelerate theinformation feedback cycle in DoE experiments [47].
Meanwhile, techniques to speed up data acquisition andanalysis via HRTEM are currently being developed thatwill permit high quality data to be taken and interpreted at
a rate commensurate with that of sample generation [48].
With such techniques in hand, one could envision thecreation of catalyst array samples where tens to hundredsof different synthesis conditions are probed to yield
information regarding their effect on particle shape, morphology, and possibly activity in a next, next generation
gas-dosing cell. Great strides have been made in performing in situ EXAFS and XANES experiments to monitor local elemental coordination in parallel, the best
example of this to date being the Miller group’s 6-shooter

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(Fig. 7) [37, 49, 50]. However, such systems often involve
substantial beam path-lengths and can only operate atambient pressure conditions, next generation XAFS/XANES high-speed instruments operating under industrially relevant conditions would significantly broaden existing understanding of how the chemical environment alters
catalyst coordination. A similar opportunity exists forin situ x-ray diffraction studies, particularly those atnational synchrotron sources, where bulk catalyst crystal
structure under elevated pressures and temperatures would
provide information on bulk structural changes applicable
to a broad range of fields. In all cases, if the ability were
present to simultaneously integrate online chemical analytics with structural and/or chemical characterization
techniques a new understanding of the link between theobserved catalyst performance and changes to its microstructure could be obtained.
Such a drastic increase in the quantity of in situ chemical, structural, and catalytic information generated per
experiment will render traditional means of analyzing data
onerous, if not impossible. Moreover, the issues of data
quality, reproducibility of results, and effective dissemination of terabyte quantities of data must be addressed tomaximize return of investment for public and private
entities. Modern HT in situ diffraction studies now produce
in excess of 100 GBs of diffraction patterns in a single daythat then need to be correlated to composition, synthesis
condition, activity, and an assortment of other measurement techniques. To accomplish this in a reasonableamount of time, one can look to the fields of bioinformatics, DFT, and functional materials for examples ofmethods of organizing, archiving and querying large-scale
databases. Databases like AflowLib and Materials Project
use JavaScript Object Notation (JSON) tags to organize
data and permit simple keyword or complex interdependencies to be searched via intuitively designed websites

Fig. 7 Schematic of the 6-shooter sample holder (green part) inin situ XAFS/XANES reactor with dimensions of the reactor marked

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[51, 52]. Examples of such databases for experimentally
determined materials properties exist such as MatNavi[53], however, the scope of such databases is often limited.
It is necessary to begin developing such a searchable
database for the HT catalysis community. A problem
arises, however, in that while calculations performed
within a single entity such as AFlowLib or Materials
Project are largely self-consistent, experimental values of aparticular property (say catalytic activity) are sensitive not
only to synthesis and processing of the catalyst, but also to
the activation and catalytic reaction condition. It is therefore a necessity to establish standard experimental procedures, and methods for systematically reporting all metadata that will permit valid cross-comparison of catalytic
properties. One such method for this would be the development of catalyst standards for important reactions andassociated reaction conditions that can be used to calibrate
individual reactors. The mandate for accumulating, curating and storing the obtained catalytic data may be mosteffective if it is assigned to an individual entity. In the US,
a government agency such as NIST with an establishedrecord of accomplishment in collaborating with industry,
an existing mandate to promote commerce, and a trackrecord of hosting materials property databases, would be anatural fit for such a database.
Merely producing a database is a meaningless enterprise, however, if there do not exist sufficient resources for
extracting new knowledge from it [54]. Moreover, performing hundreds of in situ studies on catalysts which are
not active for a particular reaction could potentially clog
the database with meaningless data. A two-pronged attack
would help to maximize the amount of information provided by each data point. Firstly, new methods of analyzing
structural, spectral, and catalytic data that utilize recent

Fig. 8 Envisioned High-throughput Experimentation Center demonstrating that common materials characterization needs being served
by a ‘‘brick and mortar’’ center. Established catalysis specific tools
would form a foundation by providing open accessAuthor's personal copy
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advances in machine learning would be of great use forestablishing correlations between composition, processing
and catalytic activity in large open databases. The functional materials community currently leads the HT field inthis respect with freely available software such as CombiView that can use a number of cluster analysis tools aswell as supervised machine learning algorithms [55–57].
Such approaches can be adopted and modified by the HT
catalysis community to provide a means of linking structure, processing, and functionality. Secondly, these techniques can not only benefit end of the experiment analysis
but can also be applied in real-time to facilitate on-the-fly
design of experiments. As XANES, XRD, and catalytic
activity data are being taken, data can be analyzed andclustered into regions of interest, permitting an optimal
distribution of data density that prioritizes not ‘‘winning
catalysts’’ but those materials and conditions that reside on
the edge of being effective.
It would also be worthwhile to establish an accepted
metric for estimating the economics of experimental
studies. Very few academic experimentalists consider costbenefit analysis in the process of designing a new set ofexperiments. While we do not suggest this criteria be usedas the basis of funding for academic research, this is a skill
set that could guide future industrial studies by providing aball park return on investment for new research projects.
On the other hand, systems level engineering perspectives
could benefit funding agencies, by providing the argument
against technologies that, though technically feasible,
would not provide sufficient impact to merit large programs. Such tools could be used to provide guidance for the
exploration of new catalytic systems by helping to determine if an exploitative study of existing catalytic materials
might be more cost-effective than an explorative study into
exotic catalysts and synthesis techniques.
To effect a large scale change in the manner by which
new materials are conceived will require the creation ofhigh throughput centers spanning large number of fields,
including catalysis (Fig. 8). Leveraging the existing network of practitioners of high-throughput experiments cancreate an ‘‘HTE materials superhighway,’’ and as a result
maximize scientific impact and return on public investment. The network can be mobilized to address the urgent,high impact, and materials-constrained technologies mentioned earlier. Moreover, although each field has its own
particular figure of merit, many of the characterization
techniques required in the different fields are common e.g.
diffraction and high resolution TEM for bulk structural
information. There are also similar gaps in the ability tocreate standards to acquire, process, archive, and effectively probe truly large databases containing data and metadata generated by disparate groups. These mutual goals
make it possible to create centralized facilities with297

expertise in designing, standardizing, and formatting
combinatorial datasets, which can then be used to inform
experimentalists and theorists across multiple fields ofresearch. Brick and mortar physical centers located atsynchrotron light sources are natural fits for generalized
structural investigations; while virtual centers specializedcatalysts synthesis and parallel bed catalytic reactors would
be established in academic and industrial labs with preexisting expertise. Centralizing data collection will help defacto in the creation of data acquisition, processing, andformatting standards, with multiplicative increases in thegeneralizability of each new data set. With these tools in
hand, a new era of accelerated catalyst discovery, optimization, and commercialization would be enabled, solving alarge number of pressing industrial and environmental
issues, while providing the data that will drive a revolution
in knowledge based catalyst design.
Acknowledgements Hattrick-Simpers would like to acknowledge
the support by the US National Science Foundation under Grant DMR
1439054.

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