Santosh K. Suram

High-throughput bubble screening method for combinatorial discovery of electrocatalysts for water splitting.

Combinatorial synthesis and screening for discovery of electrocatalysts has received increasing attention, particularly for energy-related technologies. High-throughput discovery strategies typically employ a fast, reliable initial screening technique that is able to identify active catalyst composition regions. Traditional electrochemical characterization via current-voltage measurements is inherently throughput-limited, as such measurements are most readily performed by serial screening. Parallel screening methods can yield much higher throughput and generally require the use of an indirect measurement of catalytic activity. In a water-splitting reaction, the change of local pH or the presence of oxygen and hydrogen in the solution can be utilized for parallel screening of active electrocatalysts. Previously reported techniques for measuring these signals typically function in a narrow pH range and are not suitable for both strong acidic and basic environments. A simple approach to screen the electrocatalytic activities by imaging the oxygen and hydrogen bubbles produced by the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is reported here. A custom built electrochemical cell was employed to record the bubble evolution during the screening, where the testing materials were subject to desired electrochemical potentials. The transient of the bubble intensity obtained from the screening was quantitatively analyzed to yield a bubble figure of merit (FOM) that represents the reaction rate. Active catalysts in a pseudoternary material library, (Ni-Fe-Co)Ox, which contains 231 unique compositions, were identified in less than one minute using the bubble screening method. An independent, serial screening method on the same material library exhibited excellent agreement with the parallel bubble screening. This general approach is highly parallel and is independent of solution pH.

Development of solar fuels photoanodes through combinatorial integration of Ni–La–Co–Ce oxide catalysts on BiVO4

The development of an efficient photoanode remains the primary materials challenge in the establishment of a scalable technology for solar water splitting. The typical photoanode architecture consists of a semiconductor light absorber coated with a metal oxide that serves a combination of functions, including corrosion protection, electrocatalysis, light trapping, hole transport, and elimination of deleterious recombination sites. To date, such coatings have been mostly limited to simple materials such as TiO_2 and Co-Pi, with extensive experimental and theoretical effort required to provide an understanding of the physics and chemistry of the semiconductor-coating interface. To provide a more efficient exploration of metal oxide coatings for a given light absorber, we introduce a high throughput methodology wherein a uniform BiVO_4 thin film is coated with 858 unique metal oxides covering a range of metal oxide loadings and the full Ni–La–Co–Ce oxide quaternary composition space. Photoelectrochemical characterization of each photoanode reveals that approximately one third of the coatings lower the photoanode performance while select combinations of metal oxide composition and loading provide up to a 14-fold increase in the maximum photoelectrochemical power generation for oxygen evolution in pH 13 electrolyte. Particular Ce-rich coatings also exhibit an anti-reflection effect that further amplifies the performance, yielding a 20-fold enhancement in power conversion efficiency compared to bare BiVO4. By use of in situ optical spectroscopy and comparisons between the metal oxide coatings and their extrinsic optical and electrocatalytic properties, we present a suite of data-driven discoveries, including composition regions which form optimal interfaces with BiVO4 and photoanodes that are suitable for integration with a photocathode due to their excellent power conversion and solar transmission efficiencies. The high throughput experimentation and informatics provides a powerful platform for both identifying the pertinent interfaces for further study and discovering high performance photoanodes for incorporation into efficient water splitting devices.

Generating information-rich high-throughput experimental materials genomes using functional clustering via multitree genetic programming and information theory.

High-throughput experimental methodologies are capable of synthesizing, screening and characterizing vast arrays of combinatorial material libraries at a very rapid rate. These methodologies strategically employ tiered screening wherein the number of compositions screened decreases as the complexity, and very often the scientific information obtained from a screening experiment, increases. The algorithm used for down-selection of samples from higher throughput screening experiment to a lower throughput screening experiment is vital in achieving information-rich experimental materials genomes. The fundamental science of material discovery lies in the establishment of composition-structure-property relationships, motivating the development of advanced down-selection algorithms which consider the information value of the selected compositions, as opposed to simply selecting the best performing compositions from a high throughput experiment. Identification of property fields (composition regions with distinct composition-property relationships) in high throughput data enables down-selection algorithms to employ advanced selection strategies, such as the selection of representative compositions from each field or selection of compositions that span the composition space of the highest performing field. Such strategies would greatly enhance the generation of data-driven discoveries. We introduce an informatics-based clustering of composition-property functional relationships using a combination of information theory and multitree genetic programming concepts for identification of property fields in a composition library. We demonstrate our approach using a complex synthetic composition-property map for a 5 at. % step ternary library consisting of four distinct property fields and finally explore the application of this methodology for capturing relationships between composition and catalytic activity for the oxygen evolution reaction for 5429 catalyst compositions in a (Ni-Fe-Co-Ce)Ox library.

High‐throughput synchrotron X‐ray diffraction for combinatorial phase mapping

Discovery of new materials drives the deployment of new technologies. Complex technological requirements demand precisely tailored material functionalities, and materials scientists are driven to search for these new materials in compositionally complex and often non-equilibrium spaces containing three, four or more elements. The phase behavior of these high-order composition spaces is mostly unknown and unexplored. High-throughput methods can offer strategies for efficiently searching complex and multi-dimensional material genomes for these much needed new materials and can also suggest a processing pathway for synthesizing them. However, high-throughput structural characterization is still relatively under-developed for rapid material discovery. Here, a synchrotron X-ray diffraction and fluorescence experiment for rapid measurement of both X-ray powder patterns and compositions for an array of samples in a material library is presented. The experiment is capable of measuring more than 5000 samples per day, as demonstrated by the acquisition of high-quality powder patterns in a bismuth-vanadium-iron oxide composition library. A detailed discussion of the scattering geometry and its ability to be tailored for different material systems is provided, with specific attention given to the characterization of fiber textured thin films. The described prototype facility is capable of meeting the structural characterization needs for the first generation of high-throughput material genomic searches.

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

Significance Combining high-throughput computation and experiment accelerates the discovery of photoelectrocatalysts for water oxidation and explains the origin of their functionality, establishing ternary metal vanadates as a prolific class of photoanode materials for generation of chemical fuels from sunlight. The limited number of known low-band-gap photoelectrocatalytic materials poses a significant challenge for the generation of chemical fuels from sunlight. Using high-throughput ab initio theory with experiments in an integrated workflow, we find eight ternary vanadate oxide photoanodes in the target band-gap range (1.2–2.8 eV). Detailed analysis of these vanadate compounds reveals the key role of VO4 structural motifs and electronic band-edge character in efficient photoanodes, initiating a genome for such materials and paving the way for a broadly applicable high-throughput-discovery and materials-by-design feedback loop. Considerably expanding the number of known photoelectrocatalysts for water oxidation, our study establishes ternary metal vanadates as a prolific class of photoanode materials for generation of chemical fuels from sunlight and demonstrates our high-throughput theory–experiment pipeline as a prolific approach to materials discovery.

High-throughput on-the-fly scanning ultraviolet-visible dual-sphere spectrometer

We have developed an on-the-fly scanning spectrometer operating in the UV-visible and near-infrared that can simultaneously perform transmission and total reflectance measurements at the rate better than 1 sample per second. High throughput optical characterization is important for screening functional materials for a variety of new applications. We demonstrate the utility of the instrument for screening new light absorber materials by measuring the spectral absorbance, which is subsequently used for deriving band gap information through Tauc plot analysis.

Combinatorial thin film composition mapping using three dimensional deposition profiles

Many next-generation technologies are limited by material performance, leading to increased interest in the discovery of advanced materials using combinatorial synthesis, characterization, and screening. Several combinatorial synthesis techniques, such as solution based methods, advanced manufacturing, and physical vapor deposition, are currently being employed for various applications. In particular, combinatorial magnetron sputtering is a versatile technique that provides synthesis of high-quality thin film composition libraries. Spatially addressing the composition of these thin films generally requires elemental quantification measurements using techniques such as energy-dispersive X-ray spectroscopy or X-ray fluorescence spectroscopy. Since these measurements are performed ex-situ and post-deposition, they are unable to provide real-time design of experiments, a capability that is required for rapid synthesis of a specific composition library. By using three quartz crystal monitors attached to a stage with translational and rotational degrees of freedom, we measure three-dimensional deposition profiles of deposition sources whose tilt with respect to the substrate is robotically controlled. We exhibit the utility of deposition profiles and tilt control to optimize the deposition geometry for specific combinatorial synthesis experiments.

The role of the CeO2/BiVO4 interface in optimized Fe–Ce oxide coatings for solar fuels photoanodes

Solar fuel generators entail a high degree of materials integration, and efficient photoelectrocatalysis of the constituent reactions hinges upon the establishment of highly functional interfaces. The recent application of high throughput experimentation to interface discovery for solar fuels photoanodes has revealed several surprising and promising mixed-metal oxide coatings for BiVO4. Using sputter deposition of composition and thickness gradients on a uniform BiVO4 film, we systematically explore photoanodic performance as a function of the composition and loading of Fe–Ce oxide coatings. This combinatorial materials integration study not only enhances the performance of this new class of materials but also identifies CeO2 as a critical ingredient that merits detailed study. A heteroepitaxial CeO2(001)/BiVO4(010) interface is identified in which Bi and V remain fully coordinated to O such that no surface states are formed. Ab initio calculations of the integrated materials and inspection of the electronic structure reveals mechanisms by which CeO2 facilitates charge transport while mitigating deleterious recombination. The results support the observations that addition of Ce to BiVO4 coatings greatly enhances photoelectrocatalytic activity, providing an important strategy for developing a scalable solar fuels technology.

Statistical analysis and interpolation of compositional data in materials science.

Compositional data are ubiquitous in chemistry and materials science: analysis of elements in multicomponent systems, combinatorial problems, etc., lead to data that are non-negative and sum to a constant (for example, atomic concentrations). The constant sum constraint restricts the sampling space to a simplex instead of the usual Euclidean space. Since statistical measures such as mean and standard deviation are defined for the Euclidean space, traditional correlation studies, multivariate analysis, and hypothesis testing may lead to erroneous dependencies and incorrect inferences when applied to compositional data. Furthermore, composition measurements that are used for data analytics may not include all of the elements contained in the material; that is, the measurements may be subcompositions of a higher-dimensional parent composition. Physically meaningful statistical analysis must yield results that are invariant under the number of composition elements, requiring the application of specialized statistical tools. We present specifics and subtleties of compositional data processing through discussion of illustrative examples. We introduce basic concepts, terminology, and methods required for the analysis of compositional data and utilize them for the spatial interpolation of composition in a sputtered thin film. The results demonstrate the importance of this mathematical framework for compositional data analysis (CDA) in the fields of materials science and chemistry.

Relaxation Methods for Constrained Matrix Factorization Problems: Solving the Phase Mapping Problem in Materials Discovery

Matrix factorization is a robust and widely adopted technique in data science, in which a given matrix is decomposed as the product of low rank matrices. We study a challenging constrained matrix factorization problem in materials discovery, the so-called phase mapping problem. We introduce a novel “lazy” Iterative Agile Factor Decomposition (IAFD) approach that relaxes and postpones non-convex constraint sets (the lazy constraints), iteratively enforcing them when violations are detected. IAFD interleaves multiplicative gradient-based updates with efficient modular algorithms that detect and repair constraint violations, while still ensuring fast run times. Experimental results show that IAFD is several orders of magnitude faster and its solutions are also in general considerably better than previous approaches. IAFD solves a key problem in materials discovery while also paving the way towards tackling constrained matrix factorization problems in general, with broader implications for data science.