Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

Materials are technology enablers, and the discovery and commercialization of advanced materials is crucial to solving major challenges in technological innovation, economic growth, and the environment. In 2011, President Obama announced the Materials Genome Initiative (MGI),¹ a national effort to introduce new materials into commerce more quickly (by a factor of 2, i.e., from about 10–20 years to about 5–10 years) and at a lower cost. The MGI approach requires contributions in three critical areas: computational tools, experimental tools, and digital data. Since its inception, the MGI has made significant progress in predicting the structure and properties of new functional materials through computational simulation and modeling. To build on this progress, experimental data for validating these models, and informing more sophisticated ones, will be required. High-throughput experimental (HTE) methodologies^2–10 typically employ combinatorial materials science techniques wherein materials “libraries” are synthesized and characterized to rapidly determine structural, physical, and chemical properties. Due to their ability to rapidly establish relationships between composition, structure, and functional properties, HTE methodologies are uniquely suited as the experimental complement of computational simulation and modeling.

Today's global challenges, e.g., advanced manufacturing, dependence on critical raw materials, and climate change, underscore the urgent need for novel materials solutions (Table I). Advances in HTE methodologies have been successfully demonstrated in every class of technologically important materials, and it has been demonstrated that HTE can lead to the discovery and deployment of new materials at unprecedented speed and low cost.^{4,5,7,9,11–15} However, according to the results of a recent HTE workshop,¹⁶ full realization of the MGI vision will require the integration of experiment, computation, and theory, open access to high quality digital data and materials informatics tools, and an educated workforce.

This paper reviews the opportunities and challenges facing the use of HTE for materials innovation. Advances in new materials for the energy, electronics, chemical, aerospace, and defense sectors will provide technology solutions and drive economic growth. Ultimately, a strong and widespread MGI community will establish a “materials superhighway” to address the urgent, high impact, and materials-constrained challenges facing the nation.

Major conclusions to be discussed in detail in this paper are as follows:

• Critical technologies such as energy production and utilization, microelectronics, and catalysis await immediate materials solutions through the discovery and development of higher performance photovoltaic (PV), thermoelectric, energy storage, fuel cell, semiconductor, and catalytic materials.

• Existing United States Government-funded MGI-related materials design programs (Table II), while producing excellent research, lack optimized coordination, resulting in underdeveloped opportunities and capabilities in data collection, curation, and analysis; although most elements of the required MGI infrastructure exist, distributed over government (as well as academic and industrial) laboratories, there are few mechanisms for interaction.

• Widespread utilization of HTE methodologies is required to inform and validate computational MGI efforts.

• HTE methodologies are uniquely suited to rapidly generate the large volumes of high quality materials data required to populate materials properties databases.

• Standards for library synthesis, characterization, and data curation (e.g., library architectures, data formats, and informatics) are crucial for effective and widespread use of HTE investments.

Key recommendations are as follows:

• Enable broad access to HTE methodologies and data.

• Establish collaborations that accelerate commercialization in critical advanced materials arenas including electronic materials, catalysts, and energy-related materials.

• Establish HTE facilities, physical or virtual, centralized or distributed, where researchers can implement a total MGI approach for critical and enabling classes of materials.

• Develop new library design and characterization techniques that extend the scope of HTE methodologies beyond just composition space:

  • Include measurement and characterization of surfaces and interface properties (e.g., transport, electronic, phonon, and grain boundary effects).

  • Focus efforts on specific properties relevant to end-use applications of new materials, e.g., reliability of integrated device structures.

  • Create libraries that include variations in processing parameters.

Immediate materials solutions are needed in the area of energy production, in particular, photovoltaics (PV). The PV industry has undergone tremendous growth and restructuring over the past decade and is currently dominated by Si-based polycrystalline or single-crystal wafer technologies. A few other thin film materials systems, such as CdTe and Cu(In,Ga)Se₂ (CIGS), are also being manufactured at large scale. In addition, the emergence of hybrid organic-inorganic perovskite PV absorber materials³⁰ is revolutionizing PV research and development. However, more progress is needed in increasing the efficiency, reducing the cost, and improving the reliability of the established and emerging PV technologies, all within the boundary conditions of using scalable manufacturing processes and non-critical chemical elements. This requires material innovation in light absorbers, electrical contacts, and other layers and components of PV cells and modules, all applications that can benefit from the HTE approach.

A major challenge for HTE PV research is that solar cells are very sensitive to processing conditions and materials interactions; this is one example of the need to extend HTE research beyond composition space. Screening of processing conditions is important because process-dependent defects and microstructure control the lifetime of photo-excited charge carriers, which limits the performance of solar absorbers. HTE screening of materials interactions (in addition to individual materials) is also important because solar cell performance depends on band offsets and recombination velocities at the interfaces between PV absorbers and contacts. Hence, an HTE approach to measure the effects of a variety of processing conditions and materials interactions is needed. The same argument applies to other alternative energy technologies where highly integrated, microstructure-dependent multilayer devices are used, such as solid state batteries³¹ or solid oxide fuel cells.³² 

Recently, progress has been made in addressing the HTE challenges of PV materials processing. For example, HTE screening of substrate temperature and target-substrate distance has been demonstrated for Cu₃N³³ and Cu₂O^34,35 absorbers, as well as ZnO³⁶ and In₂S₃³⁷ contacts. Methods for screening processing parameters rely on intentional continuous or discrete temperature gradients across the substrates during library synthesis,^38,39 as shown in Fig. 1(a). HTE screening has also addressed materials interactions through the fabrication of combinatorial PV device libraries with intentional composition and thickness gradients in one layer (e.g., absorber or contact) of a multi-layer stack (Fig. 1(b)). Such HTE research has been performed for a range of solar cell absorber materials, from quite mature (e.g., Cu(In,Ga)Se₂,^40,41 to emerging (e.g., Cu₂ZnSnS₄⁴² and CuSbSe₂⁴³), to very novel (e.g., Co₃O₄⁴⁴ and Cu₂O⁴⁵). For some of these absorbers, the PV efficiency was correlated with quasi-Fermi level splitting determined from photoluminescence mapping.⁴² However, more work is needed on spatially resolved characterization of other photo-excited charge carrier properties, such as bulk minority carrier lifetime or surface recombination velocity.

The recovery of waste heat using thermoelectric devices represents an enormous opportunity for materials innovation to impact energy utilization. In the United States, the industrial sector consumes approximately one third of all energy, roughly 32 × 10¹⁵ (quadrillion) Btu per year. Of this amount, between 5 and 13 quadrillion Btu per year are lost as waste heat via streams of hot exhaust liquids and gases, as well as through heat conduction, convection, and radiation from manufacturing equipment and processes.⁴⁶ Indeed, “…the United States is the Saudi Arabia of waste heat.”⁴⁷ Recent studies have shown that for the United States alone, annual potential for electrical energy recovery from waste heat could be in the multi-terawatt range.⁴⁸ Although thermoelectric devices have significant potential to recover waste heat from industrial processes, commercially available devices are only about 5% efficient. Therefore, discovery of higher efficiency thermoelectric materials using HTE is critical to enabling the practical recovery of waste heat. Materials that exhibit a large Seebeck coefficient, high electrical conductivity, and low thermal conductivity are considered candidates for use in thermoelectric applications;^49,50 optimizing these transport properties improves the energy conversion efficiency. The efficiency and performance of thermoelectric power generation are proportional to the dimensionless figure of merit, ZT, of the material. ZT = S²σT/k, where T is the absolute temperature, S is the Seebeck coefficient, σ is the electrical conductivity, and k is the thermal conductivity. High-throughput instruments capable of locally and rapidly measuring Seebeck coefficients at room^51,52 and elevated temperatures⁵³ have been constructed. Further, high-throughput measurements of thermal effusivity, from which thermal conductivity can be derived, have also been carried out, using either time domain⁵⁴ or frequency domain thermoreflectance.⁵⁵ Thus, ZT can be obtained through HTE techniques; however, the power factor, equal to S²σ, is also a suitable figure of merit and can be obtained more readily because it does not require measuring thermal conductivity. Figure 2, illustrating research performed on the Ca₃Co₄O₉ system,⁵⁶ shows the compositions on the library film that exhibit the highest power factors. HTE approaches have been applied in the search for new thermoelectric materials by diffusion annealing of bulk materials,⁵⁷ unidirectional solidification,⁵⁸ and the use of compositionally graded thin films.^51,53,55,59 Thus far, only a limited number of pseudo-binary and -ternary thermoelectric systems have been investigated using HTE: (Zn,Al)O,⁵¹ Ca₃Co₄O₉,⁵⁶ Co-Ce-Sn,⁵⁵ PbTe–Ag₂Te–Sb₂Te₃,⁵⁸ Mg_xSi_yGe_1-y,⁵⁹ CoSb₃–LaFe₄Sb₁₂–CeFe₄Sb₁₂ and Sb₂Te₃–Bi₂Te₃.⁵³ 

Energy storage materials such as in Li ion batteries represent another opportunity for the HTE approach. The exponential growth of computer processing power, combined with the laws of physics expressed through quantum mechanics, has made it possible to design new materials from first principle physics using supercomputers. In the mid-2000s, the development of high-throughput computational methods and software infrastructure was pioneered and applied to the discovery of novel energy storage materials.^60,61 Importantly, HTE synthesis and measurement techniques were also developed, which enable rapid validation and benchmarking of simulation and modeling data.^62,63 Thousands of candidate Li-ion intercalation materials were screened for suitable properties such as phase stability, ionic diffusivity, capacity, and volume expansion. Several novel compounds were identified and subsequently synthesized and tested, including a monoclinic form of LiMnBO₃,⁶⁴ layered Li₉V₃(P₂O₇)₃(PO₄)₂,⁶⁵ Cr-doped LiVO₂,⁶⁶ and a new class of Li₃MPO₄CO₃ (M = transition metal) materials, which are unconventional in that they mix two different polyanion groups (phosphate and carbonate).⁶⁷ 

Building upon the “Materials Project” infrastructure,⁶⁸ the Joint Center for Energy Storage (JCESR) launched a high-throughput computational search for multivalent ion intercalation compounds as well as novel electrolyte formulations. Multivalent intercalation cathodes can exhibit very high energy density compared to their Li-ion counterparts, creating the materials innovation challenge of identifying host structures with sufficient ionic mobility. Systematically searching through redox-active cations and mobile multivalent species, Liu et al.^69,70 predicted reasonable overall performance for Mg and Ca in the Mn oxide spinel structure (Fig. 3), as well as in the Cr, Ti, and Mn sulfide spinels. Furthermore, Rong et al.⁷¹ formulated multivalent ionic mobility design rules as a function of structure type and coordination environment. Indeed, sluggish yet reversible Mg intercalation in Mn oxide spinels was subsequently proven⁷² and, excitingly, recent work⁷³ experimentally demonstrated well-behaved Mg intercalation in the cubic TiS₂ spinel. The latter material yielded a voltage of 1.2 V and a capacity of 200 mA h/g upon cycling at 60 °C, almost double the energy density of the best performing multivalent cathode to date.⁷⁴ Voltage and diffusivity measurements of both materials agreed with the predicted behavior. For liquid electrolyte energy storage applications, the JCESR-led “Electrolyte Genome”^75,76 was developed to design novel molecular formulations for beyond-Li ion technologies. For example, an in-depth study of Mg electrolyte decomposition elucidated that strong ion pairing in Mg electrolytes, coupled with the multiple-electron charge transfer reaction at the negative electrode, leads to concentration-dependent interfacial decomposition reactions. Recent experimental work on ionic liquid Mg electrolytes⁷⁷ similarly found that rational design of solvation structures is crucial to realizing novel systems with improved stability. Similar high-throughput computational efforts to search for more stable solvents for Li-air battery applications have also been undertaken.⁷⁸ 

Furthermore, there is a growing interest in solid state energy storage, and novel compounds exhibiting superfast Li-ion conduction have been predicted via high-throughput computations,⁷⁹ and subsequently synthesized.^80,81 HTE has thus far focused on solid state electrolytes, with a strategy of small compositional changes within one structure or compound family. For example, Beal et al.⁸² screened solid solution compositions in Li_3xLa_2/3-xTiO₃ for fast Li conductivity and Yada et al.⁸³ identified promising highly conducting dielectric interlayers for solid state Li batteries in the Li-Nb-Ta ternary oxide system.

Over the last two decades, the number of elements (and therefore new materials) used in silicon microelectronics manufacturing has jumped from twelve to over fifty, as shown in Fig. 4. As device dimensions approach a few atomic layers, electronic wave functions and materials properties are dominated by surfaces and interfaces and can no longer be estimated from bulk behavior. The ability to understand and build lower power switches such as tunneling field-effect transistors, as well as interconnects with lower resistance, is fundamentally linked to an accurate understanding of atomic scale interfaces. A wealth of detailed experimental data will drive the design of complex three dimensional structures on the atomic scale. HTE has enabled advances in the microelectronics industry through development of advanced gate stack materials (high-κ gate dielectrics and metal gate electrodes) and ferroelectric, piezoelectric, multiferroic, and magnetic oxide materials.⁴ Many developments in the semiconductor industry require continuous advancement in materials, multilevel thin film stacks, and their associated properties. From dimensional scaling, traditional lithography has transitioned to multi-patterning, leveraging the confluence of deposition and etch during exposure. For example, low temperature atomic layer deposition (ALD) spacers with differing etch rates are utilized for pitch division strategies. These require screening of unique ALD ligands and ALD process parameters along with comparative etch rates and materials compatibility. Selective deposition further opens up ways to reduce patterning and integration costs.⁸⁴ 

Traditional solution-based high-throughput techniques have been adapted to screen for new resist formulations,⁸⁵ but some of the materials challenges associated with extreme ultraviolet lithography include advanced resists beyond traditional chemically amplified resists. HTE must be applied to develop metal oxide resists that provide improved sensitivity, minimal line edge and line width roughness, and etch resistance.

Another emerging device area is 2D materials⁸⁶ such as transition metal dichalcogenides,⁸⁷ ferroelectric materials for negative-gate-capacitance field effect transistors,⁸⁸ traditional field effect transistors, ferroelectric-dynamic random access memory cells,⁸⁹ resistive random access memory cells,⁸⁶ Mott field effect transistors,⁸⁶ and chalcogenide-based memories,⁸⁶ where specific materials properties are leveraged in conjunction with device behavior to enable transistor scaling of novel memory devices. Applying HTE methodologies in the aforementioned areas requires the means to process the materials in dimensionally and compositionally controlled thin film stacks, using relevant processing tools such as sputtering or ALD, and appropriate thermal budgets. Further, early device prototypes and test vehicles must be employed to more accurately and adequately assess their initial properties. Additionally, more advanced materials characterization techniques must be employed to understand the materials properties in conjunction with electrical characterization and analysis. For example, to tailor the ferroelectric behavior of HfO₂-based materials for a particular device application, one must be able to quantitatively understand the statistically relevant control parameters such as precursor ligand, oxidant, underlying substrate, doping/alloying species and concentration, electrode materials, thermal history, and annealing conditions. Such an approach is in keeping with the aforementioned need for HTE libraries to include processing variables, as opposed to simply composition, as library parameters.

The development of Low Density High Entropy Alloys (LDHEAs) could provide an important advancement in automotive fuel efficiency, and the utility of an HTE platform for exploration of these advanced materials has recently been demonstrated.⁹⁰ Once candidate materials are identified, gradient film libraries can be rapidly synthesized and characterized at every composition point across the film. These libraries can then be screened for basic structural (e.g., phase, composition (as shown in Fig. 5), grain size) and mechanical properties (e.g., hardness using nano-indentation), as well as subjected to incremental anneals to gain insight into phase stability as a function of temperature and their correlation with mechanical properties.

LDHEAs have the potential to provide dramatically improved specific yield strength with a concomitant improvement in the balance between strength and ductility of metals, corrosion resistance, and reduced sensitivity to processing conditions. However, the complex and nuanced materials and process-phase space for LDHEAs make screening and optimizing bulk alloys much more time consuming than for traditional alloys. The HTE approach has the potential to screen broad compositional regions of complex alloys quickly, thereby significantly accelerating their development and optimization.

The field of catalysis spans a broad range of applications, as optimized catalysts are often needed to realize efficient, selective chemical reactions in applications ranging from medicine to renewable fuels. In the context of HTE and the establishment of structure-property relationships, catalysis research may be conceptually separated into small-molecule and solid-state-materials categories. The deep roots and continued prevalence of HTE in pharmaceutical research enable efficient exploration for small molecule functionalization in medicinal chemistry⁹¹ and reaction discovery in synthetic chemistry.⁹² Due to the role that molecular structure plays in facilitating a chemical reaction, structure-activity relationships are prevalent in small molecule catalysis and motivate integration of HTE methods with descriptor-based catalyst design from statistical or theoretical computation.⁹³ Indeed, a hallmark achievement in the catalysis field resulted from a Dow Chemical Corporation computation-guided HTE program that yielded the now industrialized process for synthesizing InFuse™ olefin block copolymers (OBCS). This two-year project produced new theoretically predicted materials (at times more than 1600 individual polymerization reactions were evaluated during a three-week period), optimized production, and rapidly commercialized a new class of polymers now made at scales exceeding 10⁸ pounds per year.⁹⁴ 

Implementations of HTE in basic catalyst research are typically focused on their discovery and informatics. Currently, there are rapidly increasing efforts in solid-state heterogeneous catalysis due to its importance to a variety of emerging technologies, especially renewable energy technologies where earth-abundant catalysts are highly desirable.^12,95 The quality of HTE catalyst screening has significantly improved due to the recent development of HTE electrochemical cells^96–98 and detection of reaction products for both gas-phase catalysis^99,100 and electrocatalysis,¹⁰¹ which is critical for identification of catalysts with high selectivity. The discovery of heterogeneous catalysts requires particular implementations of HTE strategies for establishing structure-property relationships (of course, this same complexity argument applies to other classes of materials, e.g., photovoltaic materials); while materials properties such as band gap energy and superconducting critical temperature correspond to a particular composition and crystal structure, mixed-phase materials can enable new reaction pathways in catalysis due to effects such as spillover and catalyst-support interactions.¹⁰² As a result, the materials search space includes vast combinations of elements in high-dimensional composition spaces. An emerging application of HTE is the mapping of composition-activity relationships to discover composition spaces that exhibit unique catalytic properties, followed by the evaluation of computational predictions using such results.¹⁰³ The state-of-the-art with respect to experimental throughput has been reported by Haber et al.^104,105 where thousands of mixed metal oxides (Fig. 6) were screened as electrocatalysts for the oxygen evolution reaction, a critical component of electrolysis and solar fuels technologies. An HTE-discovered quinary oxide catalyst was found to contain nanoparticles of two different phases with atomically sharp interfaces, demonstrating the importance of phase mixtures in optimizing performance.¹⁰⁶ 

Because catalytic activity can strongly depend on morphology and the support material, novel implementations of HTE are often required to discover deployable catalysts, as demonstrated in the optimization of electrocatalyst particle size and supports,^107,108 and discovery of nanoparticle alloy electrocatalysts, with direct integration into an operational fuel cell.¹⁰⁹ The need to bridge the traditional gap between discovery and deployment underscores the immediate impact of HTE,¹⁰⁴ and as recently demonstrated by combinatorial integration of catalysts into solar fuels photoanodes, fabrication of device components can yield surprising discoveries and enhancements in performance.^110,111 The suite of HTE techniques in the catalysis field comprise perhaps the most advanced technology for rapid characterization of surfaces and interfaces, which directly addresses the key recommendations from the HTE workshop.¹⁶ An added benefit of the use of HTE is the potential to explore atypical catalyst-support formulations, which can result in significant increases in both catalytic performance and mitigation of deactivation mechanisms, an often overlooked aspect of catalyst design.^112,113

Sensor materials and devices have a long history in HTE because the complex interactions that dictate their performance and reliability are best optimized using high throughput techniques.⁷ Factors affecting the performance of sensor materials may include physical integrity of the multilayer devices, film microstructure, and contamination levels. Further, the operation and reliability of sensors may be sensitive to humidity and temperature. HTE can be used to rapidly characterize variations in sensor response, as well as validate optimal sensor designs. Sensors are essentially transducers, used to convert a signal of one type (chemical, electrical, optical, etc.) into a signal of another type. In mechanical sensors changes in mass on a cantilever device,¹¹⁴ for example, may be sensed as a deflection, by optical means such as a laser.¹¹⁵ Further, electrical sensors may undergo changes in resistance or capacitance as a result of, for example, a sensor-chemical interaction.¹¹⁶ 

The majority of sensors explored by HTE have been made of polymers, due to the relative ease and low cost of fabrication. Of these, the most common polymers utilized were formulated with fluorescent materials for fast optical responses or conductive polymers for fast electrical responses. Semiconducting metal oxide sensors that exhibit changes in electrical resistance as a result of chemical reduction, for example, have also been developed. Growth in the nascent “Internet of Things”¹¹⁷ will accelerate development of entire new classes of sensors, which in turn will require advanced electronic technologies including flexible, post-CMOS (e.g., graphene) devices.

The HTE approach consists of three major steps, shown in Fig. 7: (a) hypothesis-driven design and synthesis of a “library” sample with variations in the materials parameter(s) of interest^118–121 (typically composition); (b) rapid, local, and automated interrogation of the library for the properties of interest;^53,122–128 and (c) analysis, mining, display, and curation of the resultant data.^129–131 Each step presents current challenges that must be overcome before HTE methodologies can be widely deployed.

Library synthesis and metrology tools are often expensive and not readily available commercially and therefore not accessible to most materials researchers. Currently, HTE libraries and measurement tools are primarily designed with the goal of materials discovery and optimization; therefore, composition is the most common variable HTE parameter. Further, library samples are not usually deposited using the processing tools and conditions eventually required to produce the intended material or device. Library design must evolve so that the libraries increasingly represent the actual materials and processing conditions that the selected materials will be exposed to during device production. Solar cells, fuel cells, and transistors, for example, all require the integration of multiple materials with different functionalities. These devices all contain numerous interfaces, such as the p-n junction in photovoltaics, or the solid-water catalytic interface in photoelectric water splitting. The properties of these interfaces are the defining functional properties of the devices enabled by the constituent materials. Therefore, HTE techniques should be additionally applied to processing parameters, interface engineering, and performance of multilayer stacks/devices. Foundational demonstrations of HTE screening for integrated materials have been made in the fields of photovoltaics^37,134 and solar fuels.¹³⁵ To further the evolution of HTE technologies for component-level materials development, it is necessary to develop HTE metrologies that measure interface properties (e.g., electronic, magnetoelectric, and photonic) and grain boundary effects on device performance and reliability.

Library synthesis and metrology tools require dedicated staff to operate, calibrate, and maintain them. HTE characterization tools for composition and materials structure, e.g., X-ray fluorescence and X-ray diffractometers, are available at synchrotron beam lines and some laboratories, but HTE methods to characterize surfaces, interfaces, or chemical bonding are not widely deployed. Finally, while HTE synthesis tools have been optimized for the production of thin films, many new bulk materials are in demand, including lightweight structural materials for transportation and rare earth-free magnetic materials for electrical power generation in direct-drive wind turbines. There is, therefore, a need for the development of HTE bulk materials synthesis techniques.

For HTE to effectively address MGI goals, the ever increasing amount of data generated must be curated, analyzed, and mined to transform it into knowledge. The HTE workshop report¹⁶ recognized HTE methodologies to be uniquely suited to rapidly generate the large volumes of high-quality data required to populate materials databases. Currently, a lack of high quality materials data, and the difficulty of accessing that data, presents a barrier to the MGI scheme.¹²⁹ Many experimental and computational materials property databases exist (see Table III for a partial list), but often the data are not in an interoperable format nor they are certified, vetted, or validated. Most importantly, the metadata is often incomplete or absent, making it difficult to search and compare available data.

The HTE workshop report¹⁶ also noted that opportunities and capabilities in data capture, curation, and analysis are underdeveloped. The problems broadly affecting the community are as follows: (i) instrument output data formats are diverse, poorly described, and often not machine readable except by vendor-specific software and (ii) material measurement data are complex, with conditions and variables unique to a specific body of research. While many data repositories have flourished for specific techniques, such as high-throughput calculations,^65,139,168 general file repositories tailored to materials science,¹⁶⁹ with rich metadata,¹⁷⁰ would benefit the broader materials community.^171–174 Additionally, while for-profit materials information management and analysis providers are available to the materials community,^{144,175–177} a deliberate effort must be mounted to develop community data and metadata standards to enable widespread, interoperable data exchange.¹⁷⁸ 

NIST's¹⁷⁹ Information Technology¹⁸⁰ and Material Measurement Laboratories,¹⁸¹ for one, are developing software underpinning the “Materials Innovation Infrastructure,” including the “Materials Resource Registry (MRR)”¹⁸² and the “Materials Data Curation System (MDCS).”^183,184 The MRR allows global searches for resources (e.g., a HTE data repository), where each resource may have different access protocols. The MDCS is designed to enable community data and metadata standards and allows a search for individual results across distributed networks of HTE repositories, based on MDCS software.

Many instances of HTE facilities and data platforms exist (see examples in Table II), but they are distributed over government, academic and industrial enterprises, with few mechanisms for interaction. Currently, there are no national physical facilities dedicated to a comprehensive and sustained (one- to five-year) HTE approach for novel materials discovery and commercialization. A major goal, therefore, should be an effort to deploy a federated network of high-throughput synthesis and characterization tools and repositories and registries for experimental data, which enable integration with the existing infrastructure for computational materials science. A “HTE Materials Virtual Laboratory (HTE-MVL)”¹⁸⁵ would accelerate the development of new materials via HTE methodologies, integrated computational tools, and a data infrastructure that complements and accelerates the research process. The library synthesis and sample characterization equipment, as well as the data registries, would be geographically distributed in such a model.

The HTE-MVL could be the platform for a national or international infrastructure that functions as illustrated in Fig. 8, in which the virtual laboratory is created by adopting and integrating a number of localized HTE synthesis and characterization tools as well as materials data infrastructure tools. Most of the data tools are open-source and currently exist or are under active development. The virtual laboratory would be deployed in stages starting now, and could, for example, be an enabling concept for the US DOE's recent Energy Materials Network programs,¹⁸⁶ e.g., LightMAT, CaloriCool, and ElectroCat. Data infrastructure products could be integrated with HTE research equipment throughout the lifecycle of the traveling combinatorial sample library. Member institutions, responsible for the synthesis and characterization of sample libraries, would have to deploy systems for (i) laboratory information management, (ii) domain-specific and/or user-defined structured data and metadata management, and (iii) endpoints to a data transfer grid. Features (i) and (ii) can be addressed by two similar open-source efforts, the Integrated Collaborative Environment¹⁸⁷ (ICE) and the Timely and Trustworthy Curating and Coordinating Data Framework¹⁸⁸ (T2C2), which are developing robust and comprehensive platforms to automate data capture and accelerate materials research within a large facility. If a member institution already has a system for feature (i) but lacks a system for feature (ii), the Materials Data Curation System^183,184 (MDCS) could complement an existing infrastructure. Finally, Globus¹⁷¹ is well suited for transfer of data among institutions, including the user's home institution.

A dedicated HTE Resource Registry, which would be discoverable from the NIST Materials Resource Registry¹⁸² (MRR), would allow for global tracking of all data/metadata associated with a given library sample. A registry record for a sample library would enumerate unique resolvable identifiers (e.g., handles¹⁸⁹) for all data/metadata records associated with the sample library. Note that a handle is merely a pointer to a dataset, which could be private and behind a firewall. Furthermore, a single handle would be assigned to the sample library registry record, which would enable one-step discovery of all data and metadata associated with a sample library. HTE instruments would also be registered, which would enable optimal design of experiment across the federated network of instruments and institutions. Furthermore, the handles associated with samples and instruments could be transformed into Quick Response¹⁹⁰ (QR) codes, as shown in Fig. 9. This would enable the development of new tablet applications that enable automated association of the sample library to new measurement data, thus integrating an electronic lab notebook device with the laboratory data infrastructure.¹⁹¹ A number of opportunities exist for making HTE data (at least for the case of publically funded data) discoverable, accessible, and interoperable. At a high level, HTE repositories and registries would be discoverable via the NIST MRR.¹⁸² Individual HTE datasets would be searchable at the individual HTE repositories and registries, with faceted search optimized for the HTE community. In terms of accessibility, a number of centralized resources allow for data dissemination,^172,176 including one that is based on the Globus infrastructure.^192,193 However, individual HTE research groups may wish to deploy and maintain their own repositories, and the MDCS¹⁸⁴ software could enable them to do so. Finally, to enable interoperability, NIST is developing modular community data standards and coordinating adoption among various projects, including MDCS,¹⁸⁴ ICE,¹⁸⁷ and T2C2.¹⁸⁸ 

When materials registries, repositories, and curation systems are in place and networked, the materials community will encounter an exponential growth in the volume of accessible data. For example, the National Synchrotron Light Source II at Brookhaven National Laboratory is expected to generate between 15 and 20 petabytes of materials data per year.¹⁹⁴ Appropriate application of machine learning¹⁹⁵ techniques will be critical to capitalize on the scientific value of such large data volumes. Analysis can be performed by the vast array of machine learning algorithms that have successfully extracted actionable knowledge in a range of fields including social networks,¹⁹⁶ genetics,¹⁹⁷ and finance.¹⁹⁸ Similarly, it can be used to mine materials data, for example, composition-structure relationships, from large amounts of computational and experimental materials data, as is illustrated in Fig. 10. The benefits of machine learning for accelerated materials data analysis have already been realized, with numerous studies showing the great potential for research and discovery.^199–201 These studies include a wide range of materials analysis challenges including crystal structure^202–204 and phase diagram^{130,205–207} determination, materials property predictions,^208,209 micrograph analysis,^210,211 development of interatomic potentials^212–214 and energy functionals²¹⁵ to improve materials simulations, and on-the-fly data analysis of high-throughput experiments.²¹⁶ 

Scientific initiatives have been efficient drivers to train and educate future workforces. The multidisciplinary Nanotechnology Initiative²¹⁷ is a good example and model for MGI. The classroom is a necessary element of the MGI, where a comprehensive Lab-to-Market-to-Classroom (LMC) paradigm²¹⁸ allows for the delivery of practical, hands-on MGI-type content. The LMC concept builds upon the effectiveness of interactive, project-based learning,²¹⁹ which are strongly student-driven and ideal for instilling MGI principles in students. HTE methodologies present a substantial educational opportunity, i.e., the potential to invest in synthesis, measurement, and data science for materials discovery and commercialization, for the next generation of materials scientists. Universities are crucial for a diverse and flexible MGI-ready workforce. Opportunities in this area include open access educational resources such as web-based video tutorials on experimental best practices, experimental and data-driven materials courses, and MGI lectures, all based on emerging materials problems. Further, creating opportunities for students and faculty to contribute in industry and government labs will strengthen the “lab-to-market” pipeline by exposing them to development and production challenges, providing academic expertise in industrial settings, and promoting commercialization of university research. This can be facilitated by providing access to the HTE-MVL in an analogous way as for other national user facilities (e.g., synchrotron beamlines and neutron sources). Data Science (data acquisition, curation, database construction, data mining, and machine learning algorithms) has been identified as a critically missing element in current materials science curricula.

The authors propose the following strategic action items to enable HTE, a key component of the MGI, to significantly contribute to accelerated materials discovery and commercialization (these action items are the opinions of the authors, not necessarily the organization they represent):

1. Identify critical technologies that are currently materials-constrained, such as cost-effective batteries, ultra-high temperature metals for turbines in next-generation natural gas power plants, low cost and high efficiency solar cells, low power multiplexed sensors for flexible electronics, CO₂ hydrogenation and selective methane oxidation catalysts, chemical and biological sensors, and earth abundant substitute materials for critical elements.

2. Establish key specific targets for desired materials properties and performance and fund a “HTE Materials Virtual Laboratory” with dedicated teams and infrastructure for tackling specific materials topics that hold promise for immediate impacts (e.g., within three years).

3. Enable user access to the HTE-MVL through an on-line submission process. A flexible array of manufacturing grade HTE tools should be available to academic groups and the larger community, especially small companies and startups with the need for rapid progress; access to such a system could foster a “lab-to-fab” culture for this segment of the community.

4. Ensure that data science and management are integral to the HTE-MVL and connected to the best computational tools for large data analysis, including machine learning. All data sets and databases should be interoperable, incorporate an application programming interface (API), and use common formats, analysis protocols and informatics platforms.

5. Establish HTE standards. Standardized R and D platforms (both physical and virtual) for testing libraries and metrology tools will be required. Standard library formats should be developed.

6. Develop new chemical and physical HTE metrologies within the HTE-MVL. Further, HTE versions of standard characterization tools, such as x-ray fluorescence, micron-scale x-ray beams, atom probes, XPS, and in situ synthesis monitoring, for example, are needed.

We are grateful to Alex King, John Newsam, John Perkins, Abhijit V. Shevade, John Smythe, and Ji-Cheng Zhao for manuscript input, and Andrey Dobrynin, Tom Kalil, Om Nalamasu, Nag Patibandla, Shannon Sullivan, and the National Science Foundation (DMR Grant No. 1439054) for their critical role in making the workshop¹⁶ possible. J.M.G. acknowledges support from the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy Award No. DE-SC0004993. A.Z. was supported by U.S. DOE, as a part of a Laboratory Directed Research and Development (LDRD) program, under Contract No. DE-AC36-08GO28308 to NREL.

Certain commercial equipment, instruments, or materials are identified in this paper to adequately specify the experimental procedure. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.