Discovery of complex oxides via automated experiments and data science.

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.

Advanced and In Situ Analytical Methods for Solar Fuel Materials.

In situ and operando techniques can play important roles in the development of better performing photoelectrodes, photocatalysts, and electrocatalysts by helping to elucidate crucial intermediates and mechanistic steps. The development of high throughput screening methods has also accelerated the evaluation of relevant photoelectrochemical and electrochemical properties for new solar fuel materials. In this chapter, several in situ and high throughput characterization tools are discussed in detail along with their impact on our understanding of solar fuel materials.

Automated monitoring of electrocatalyst corrosion as a function of electrochemical history and electrolyte formulation.

Automated platforms assessing the stability of electrocatalysts are key to accelerate the deployment of clean energy technologies. Here, we present a robust system that allows the study of corrosion behavior in conjunction with the electrochemical protocol and electrolyte composition over many individual electrodes. Oxygen reduction reaction on Pt is used as a proof-of-concept platform, where the influence of the potential window and phosphoric acid (PA) addition on Pt dissolution is probed. A total of 72 hours of automated operation was realized with actions including liquid management, cell cleaning, aliquoting, PA injection, and bubble detection and removal, demonstrating further advancements in automated stability testing for electrocatalysts.

Bi Alloying into Rare Earth Double Perovskites Enhances Synthesizability and Visible Light Absorption.

A high throughput combinatorial synthesis utilizing inkjet printing of precursor inks was used to rapidly evaluate Bi-alloying into double perovskite oxides for enhanced visible light absorption. The fast visual screening of photo image scans of the library plates identifies 4-metal oxide compositions displaying an increase in light absorption, which subsequent UV-vis spectroscopy indicates is due to bandgap reduction. Structural characterization by X-ray diffraction (XRD) and Raman spectroscopy demonstrates that the visually darker composition range contains Bi-alloyed Sm2MnNiO6 (double perovskite structure), of the form (Bi,Sm)2MnNiO6. Bi alloying not only increases the visible absorption but also facilitates crystallization of this structure at the relatively low annealing temperature of 615 °C. Investigation of additional seven combinations of a rare earth (RE) and a transition metal (TM) with Bi and Mn indicates that Bi-alloying on the RE site occurs with similar effect in the family of rare earth oxide double perovskites.

Benchmarking the acceleration of materials discovery by sequential learning.

Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any "good" material, discovery of all "good" materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery.

Combinatorial Synthesis of Oxysulfides in the Lanthanum-Bismuth-Copper System.

Establishing synthesis methods for a target material constitutes a grand challenge in materials research, which is compounded with use-inspired specifications on the format of the material. Solar photochemistry using thin film materials is a promising technology for which many complex materials are being proposed, and the present work describes application of combinatorial methods to explore the synthesis of predicted La-Bi-Cu oxysulfide photocathodes, in particular alloys of LaCuOS and BiCuOS. The variation in concentration of three cations and two anions in thin film materials, and crystallization thereof, is achieved by a combination of reactive sputtering and thermal processes including reactive annealing and rapid thermal processing. Composition and structural characterization establish composition-processing-structure relationships that highlight the breadth of processing conditions required for synthesis of LaCuOS and BiCuOS. The relative irreducibility of La oxides and limited diffusion indicate the need for high temperature processing, which conflicts with the temperature limits for mitigating evaporation of Bi and S. Collectively the results indicate that alloys of these phases will require reactive annealing protocols that are uniquely tailored to each composition, motivating advancement of dynamic processing capabilities to further automate discovery of synthesis routes.

Thin-film growth of low temperature lead antimony sulfide plagionite phases.

Smooth and compact Pb5Sb8S17 and Pb9Sb8S21 thin films were synthesized via sulfurization of unique layered precursor films of amorphous (Sb,S) and crystalline PbS; our syntheses suggest that these plagionite group phases are metastable and indicate that their formation does not require hydrogen incorporation.

Discovery of Fe-Ce Oxide/BiVO4 Photoanodes through Combinatorial Exploration of Ni-Fe-Co-Ce Oxide Coatings.

An efficient photoanode is a prerequisite for a viable solar fuels technology. The challenges to realizing an efficient photoanode include the integration of a semiconductor light absorber and a metal oxide electrocatalyst to optimize corrosion protection, light trapping, hole transport, and photocarrier recombination sites. To efficiently explore metal oxide coatings, we employ a high-throughput methodology wherein a uniform BiVO4 film is coated with 858 unique metal oxide coatings covering a range of metal oxide loadings and the full (Ni-Fe-Co-Ce)Ox pseudoquaternary composition space. Photoelectrochemical characterization of the photoanodes reveals that specific combinations of metal oxide composition and loading provide up to a 13-fold increase in the maximum photoelectrochemical power generation for oxygen evolution in pH 13 electrolyte. Through mining of the high-throughput data we identify composition regions that form improved interfaces with BiVO4. Of particular note, integrated photoanodes with catalyst compositions in the range Fe(0.4-0.6)Ce(0.6-0.4)Ox exhibit high interface quality and excellent photoelectrochemical power conversion. Scaled-up inkjet-printed electrodes and photoanodic electrodeposition of this composition on BiVO4 confirms the discovery and the synthesis-independent interface improvement of (Fe-Ce)Ox coatings on BiVO4.

Effect of ac electrodeposition conditions on the growth of high aspect ratio copper nanowires in porous aluminum oxide templates.

The effect of several deposition parameters on the uniformity of copper electrodeposition through the alumina barrier layer into porous aluminum oxide templates grown in sulfuric or oxalic acid was systematically investigated. A fractional factorial design of experiment was conducted to find suitable deposition conditions among the variables: frequency, voltage, pulsed or continuous deposition, electrolyte concentration, and barrier layer thinning voltage. Continuous ac sine wave deposition conditions yielded excellent uniformity of pore-filling but damaged the porous aluminum oxide templates when deposition was continued to grow bulk copper on the surface. Pulsed electrodeposition yielded comparable uniformity of pore-filling and no damage to the porous aluminum oxide templates, even when bulk copper was deposited on them. Further optimization of pulsed deposition conditions was accomplished by comparing square and sine waveforms and pulse polarity. Pulsed square waveforms produced better pore-filling than pulsed sine waveforms. For sine wave depositions, the oxidative/reductive pulse polarity was more efficient than the commonly used reductive/oxidative pulse polarity. For square wave depositions into sulfuric acid grown pores, the reductive/oxidative pulse polarity produces more uniform pore-filling, likely as a result of enhanced resonant tunneling through the barrier layer and reoxidation of copper in faster filling pores.

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.

Parallel electrochemical treatment system and application for identifying acid-stable oxygen evolution electrocatalysts.

Many energy technologies require electrochemical stability or preactivation of functional materials. Due to the long experiment duration required for either electrochemical preactivation or evaluation of operational stability, parallel screening is required to enable high throughput experimentation. Imposing operational electrochemical conditions to a library of materials in parallel creates several opportunities for experimental artifacts. We discuss the electrochemical engineering principles and operational parameters that mitigate artifacts in the parallel electrochemical treatment system. We also demonstrate the effects of resistive losses within the planar working electrode through a combination of finite element modeling and illustrative experiments. Operation of the parallel-plate, membrane-separated electrochemical treatment system is demonstrated by exposing a composition library of mixed-metal oxides to oxygen evolution conditions in 1 M sulfuric acid for 2 h. This application is particularly important because the electrolysis and photoelectrolysis of water are promising future energy technologies inhibited by the lack of highly active, acid-stable catalysts containing only earth abundant elements.

Colorimetric screening for high-throughput discovery of light absorbers.

High-throughput screening is a powerful approach for identifying new functional materials in unexplored material spaces. With library synthesis capable of producing 10(5) to 10(6) samples per day, methods for material screening at rates greater than 1 Hz must be developed. For the discovery of new solar light absorbers, this throughput cannot be attained using standard instrumentation. Screening certain properties, such as the bandgap, are of interest only for phase pure materials, which comprise a small fraction of the samples in a typical solid-state material library. We demonstrate the utility of colorimetric screening based on processing photoscanned images of combinatorial libraries to quickly identify distinct phase regions, isolate samples with desired bandgap, and qualitatively identify samples that are suitable for complementary measurements. Using multiple quaternary oxide libraries containing thousands of materials, we compare colorimetric screening and UV-vis spectroscopy results, demonstrating successful identification of compounds with bandgap suitable for solar applications.

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.