High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments.

Electrochemistry has been used for decades to study materials' degradation in situ in corrosive environments, whether it is in room-temperature chemically aggressive solutions containing halide ions or in high-temperature oxidizing media such as pressurized water, liquid metals, or molten salts. Thus, following the recent surge in high-throughput techniques in materials science, it seems quite natural that high-throughput electrochemistry is being considered to study materials' degradation in extreme environments, with the objective to reduce corrosion resistant alloy development time by orders of magnitude and identify complex degradation mechanisms. However, while there has been considerable interest in the development of high-throughput methods for accelerating the discovery of corrosion resistant materials in different environments, these extreme environments propose formidable and exciting challenges for high-throughput electrochemical instrumentation, characterization, and data analysis. It is the objective of this paper to highlight those challenges, to present relatively new efforts to tackle them, and to develop research perspectives on the future of this exciting field. This Perspective is articulated around four main interconnected topics, which must be conjointly considered to enable corrosion resistant alloy design using high-throughput electrochemical methods: (1) high-throughput processing methods to develop material libraries, (2) high-throughput electrochemical methods for corrosion testing and evaluation, (3) high-throughput machine-learning augmented electrochemical data analysis, and (4) high-throughput autonomous electrochemistry representing the future of accelerated electrochemistry research.

Aggressively optimizing validation statistics can degrade interpretability of data-driven materials models.

One of the key factors in enabling trust in artificial intelligence within the materials science community is the interpretability (or explainability) of the underlying models used. By understanding what features were used to generate predictions, scientists are then able to critically evaluate the credibility of the predictions and gain new insights. Here, we demonstrate that ignoring hyperparameters viewed as less impactful to the overall model performance can deprecate model explainability. Specifically, we demonstrate that random forest models trained using unconstrained maximum depths, in accordance with accepted best practices, often can report a randomly generated feature as being one of the most important features in generated predictions for classifying an alloy as being a high entropy alloy. We demonstrate that this is the case for impurity, permutation, and Shapley importance rankings, and the latter two showed no strong structure in terms of optimal hyperparameters. Furthermore, we demonstrate that, for the case of impurity importance rankings, only optimizing the validation accuracy, as is also considered standard in the random forest community, yields models that prefer the random feature in generating their predictions. We show that by adopting a Pareto optimization strategy to model performance that balances validation statistics with the differences between the training and validation statistics, one obtains models that reject random features and thus balance model predictive power and explainability.

A high-throughput investigation of Fe-Cr-Al as a novel high-temperature coating for nuclear cladding materials.

High-temperature alloy coatings that can resist oxidation are urgently needed as nuclear cladding materials to mitigate the danger of hydrogen explosions during meltdown. Here we apply a combination of computationally guided materials synthesis, high-throughput structural characterization and data analysis tools to investigate the feasibility of coatings from the Fe­Cr­Al alloy system. Composition-spread samples were synthesized to cover the region of the phase diagram previous bulk studies have identified as forming protective oxides. The metallurgical and oxide phase evolution were studied via in situ synchrotron glancing incidence x-ray diffraction at temperatures up to 690 K. A composition region with an Al concentration greater than 3.08 at%, and between 20.0 at% and 32.9 at% Cr showed the least overall oxide growth. Subsequently, a series of samples were deposited on stubs and their oxidation behavior at 1373 K was observed. The continued presence of a passivating oxide was confirmed in this region over a period of 6 h.

One-step production of long-chain hydrocarbons from waste-biomass-derived chemicals using bi-functional heterogeneous catalysts.

In this study, we demonstrate the production of long-chain hydrocarbons (C8+) from 2-methylfuran (2MF) and butanal in a single step reactive process by utilizing a bi-functional catalyst with both acid and metallic sites. Our approach utilizes a solid acid for the hydroalkylation function and as a support as well as a transition metal as hydrodeoxygenation catalyst. A series of solid acids was screened, among which MCM-41 demonstrated the best combination of activity and stability. Platinum nanoparticles were then incorporated into the MCM-41. The Pt/MCM-41 catalyst showed 96% yield for C8+ hydrocarbons and the catalytic performance was stable over four reaction cycles of 20 hour each. The reaction pathways for the production of long-chain hydrocarbons is probed with a combination of infrared spectroscopy and steady-state reaction experiments. It is proposed that 2MF and butanal go through hydroalkylation first on the acid site followed by hydrodeoxygenation to produce the hydrocarbon fuels.

Exploiting redundancy in large materials datasets for efficient machine learning with less data.

Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95% of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the "bigger is better" mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume.

Integrated High-Throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion-Resistant Alloys.

Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here an integrated approach is presented using a set of high-throughput (HTP) alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of CrFeMnNi alloys are evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by 2 to 3 orders of magnitude. Based on the obtained results, a sacrificial protection mechanism is unveiled in the corrosion of CrFeMnNi alloys in molten salts which can be applied to protect the less unstable elements in the alloy from being depleted, and provided new insights on the design of high-temperature molten salt corrosion-resistant alloys.

A combinatorial characterization scheme for high-throughput investigations of hydrogen storage materials.

In order to increase measurement throughput, a characterization scheme has been developed that accurately measures the hydrogen storage properties of materials in quantities ranging from 10 ng to 1 g. Initial identification of promising materials is realized by rapidly screening thin-film composition spread and thickness wedge samples using normalized IR emissivity imaging. The hydrogen storage properties of promising samples are confirmed through measurements on single-composition films with high-sensitivity (resolution <0.3 µg) Sievert's-type apparatus. For selected samples, larger quantities of up to ∼100 mg may be prepared and their (de)hydrogenation and micro-structural properties probed via parallel in situ Raman spectroscopy. Final confirmation of the hydrogen storage properties is obtained on ∼1 g powder samples using a combined Raman spectroscopy/Sievert's apparatus.

Characterization data of an (AlFeNiTiVZr)1-xCrx multi-principal element alloy continuous composition spread library.

The data provided in this article is related to the research article entitled "Phase stabilization and oxidation of a continuous composition spread multi-principal element (AlFeNiTiVZr)1-xCrx alloy" [1]. This data article describes the high-throughput synthesis and characterization processes of an (AlFeNiTiVZr)1-xCrx alloy system. Continuous composition spread (CCS) thin-film libraries were synthesized by co-depositing an AlFeNiTiVZr metal alloy target and Cr target via magnetron sputtering. Post-processing was performed on the sample libraries with a vacuum anneal at 873 K and an air anneal at 873 K. Compositional data was determined via WDS in order to verify parameters provided by an in-house sputter model. Crystallographic data was captured via synchrotron diffraction and diffractograms were compared as a function of the change in Cr concentration. These measurements were taken in order to observe phase behavior after oxidation throughout the composition library. Furthermore, vibrational spectrographic data is provided of the oxidized library to show surface speciation along the composition gradient of the alloy system. The structural and oxidative behavior of the (AlFeNiTiVZr)1-xCrx alloy can be analysed using the data provided in this article. Additionally, this characterization dataset can be utilized in machine learning algorithms for determining important features and parameters for future hypothesis generation of functional multi-principal element alloys (MPEAs).

Exploring the First High-Entropy Thin Film Libraries: Composition Spread-Controlled Crystalline Structure.

Thin films of two types of high-entropy oxides (HEOs) have been deposited on 76.2 mm Si wafers using combinatorial sputter deposition. In one type of the oxides, (MgZnMnCoNi)Ox, all the metals have a stable divalent oxidation state and similar cationic radii. In the second type of oxides, (CrFeMnCoNi)Ox, the metals are more diverse in the atomic radius and valence state, and have good solubility in their sub-binary and ternary oxide systems. The resulting HEO thin films were characterized using several high-throughput analytical techniques. The microstructure, composition, and electrical conductivity obtained on defined grid maps were obtained for the first time across large compositional ranges. The crystalline structure of the films was observed as a function of the metallic elements in the composition spreads, that is, the Mn and Zn in (MgZnMnCoNi)Ox and Mn and Ni in (CrFeMnCoNi)Ox. The (MgZnMnCoNi)Ox sample was observed to form two-phase structures, except single spinel structure was found in (MgZnMnCoNi)Ox over a range of Mn > 12 at. % and Zn < 44 at. %, while (CrFeMnCoNi)Ox was always observed to form two-phase structures. Composition-controlled crystalline structure is not only experimentally demonstrated but also supported by density function theory calculation.

A High-Throughput Structural and Electrochemical Study of Metallic Glass Formation in Ni-Ti-Al.

On the basis of a set of machine learning predictions of glass formation in the Ni-Ti-Al system, we have undertaken a high-throughput experimental study of that system. We utilized rapid synthesis followed by high-throughput structural and electrochemical characterization. Using this dual-modality approach, we are able to better classify the amorphous portion of the library, which we found to be the portion with a full width at half maximum (fwhm) of >0.42 Å-1 for the first sharp X-ray diffraction peak. Proper phase labeling is important for future machine learning efforts. We demonstrate that the fwhm and corrosion resistance are correlated but that, while chemistry still plays a role in corrosion resistance, a large fwhm, attributed to a glassy phase, is necessary for the highest corrosion resistance.

On-the-fly closed-loop materials discovery via Bayesian active learning.

Active learning-the field of machine learning (ML) dedicated to optimal experiment design-has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.

The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.

An Inter-Laboratory Study of Zn-Sn-Ti-O Thin Films using High-Throughput Experimental Methods.

High-throughput experimental (HTE) techniques are an increasingly important way to accelerate the rate of materials research and development for many technological applications. However, there are very few publications on the reproducibility of the HTE results obtained across different laboratories for the same materials system, and on the associated sample and data exchange standards. Here, we report a comparative study of Zn-Sn-Ti-O thin films materials using high-throughput experimental methods at National Institute of Standards and Technology (NIST) and National Renewable Energy Laboratory (NREL). The thin film sample libraries were synthesized by combinatorial physical vapor deposition (cosputtering and pulsed laser deposition) and characterized by spatially resolved techniques for composition, structure, thickness, optical, and electrical properties. The results of this study indicate that all these measurement techniques performed at two different laboratories show excellent qualitative agreement. The quantitative similarities and differences vary by measurement type, with 95% confidence interval of 0.1-0.2 eV for the band gap, 24-29 nm for film thickness, and 0.08 to 0.37 orders of magnitude for sheet resistance. Overall, this work serves as a case study for the feasibility of a High-Throughput Experimental Materials Collaboratory (HTE-MC) by demonstrating the exchange of high-throughput sample libraries, workflows, and data.

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments.

With more than a hundred elements in the periodic table, a large number of potential new materials exist to address the technological and societal challenges we face today; however, without some guidance, searching through this vast combinatorial space is frustratingly slow and expensive, especially for materials strongly influenced by processing. We train a machine learning (ML) model on previously reported observations, parameters from physiochemical theories, and make it synthesis method-dependent to guide high-throughput (HiTp) experiments to find a new system of metallic glasses in the Co-V-Zr ternary. Experimental observations are in good agreement with the predictions of the model, but there are quantitative discrepancies in the precise compositions predicted. We use these discrepancies to retrain the ML model. The refined model has significantly improved accuracy not only for the Co-V-Zr system but also across all other available validation data. We then use the refined model to guide the discovery of metallic glasses in two additional previously unreported ternaries. Although our approach of iterative use of ML and HiTp experiments has guided us to rapid discovery of three new glass-forming systems, it has also provided us with a quantitatively accurate, synthesis method-sensitive predictor for metallic glasses that improves performance with use and thus promises to greatly accelerate discovery of many new metallic glasses. We believe that this discovery paradigm is applicable to a wider range of materials and should prove equally powerful for other materials and properties that are synthesis path-dependent and that current physiochemical theories find challenging to predict.

Demonstration of magnetoelectric scanning probe microscopy.

A near-field room temperature scanning magnetic probe microscope has been developed using a laminated magnetoelectric sensor. The simple trilayer longitudinal-transverse mode sensor, fabricated using Metglas as the magnetostrictive layer and polyvinylidene fluoride as the piezoelectric layer, shows an ac field sensitivity of 467+/-3 microV/Oe in the measured frequency range of 200 Hz-8 kHz. The microscope was used to image a 2 mm diameter ring carrying an ac current as low as 10(-5) A. ac fields as small as 3 x 10(-10) T have been detected.

Automated Phase Segmentation for Large-Scale X-ray Diffraction Data Using a Graph-Based Phase Segmentation (GPhase) Algorithm.

The creation of composition-processing-structure relationships currently represents a key bottleneck for data analysis for high-throughput experimental (HTE) material studies. Here we propose an automated phase diagram attribution algorithm for HTE data analysis that uses a graph-based segmentation algorithm and Delaunay tessellation to create a crystal phase diagram from high throughput libraries of X-ray diffraction (XRD) patterns. We also propose the sample-pair based objective evaluation measures for the phase diagram prediction problem. Our approach was validated using 278 diffraction patterns from a Fe-Ga-Pd composition spread sample with a prediction precision of 0.934 and a Matthews Correlation Coefficient score of 0.823. The algorithm was then applied to the open Ni-Mn-Al thin-film composition spread sample to obtain the first predicted phase diagram mapping for that sample.

The Different Roles of Entropy and Solubility in High Entropy Alloy Stability.

Multiprincipal element high entropy alloys stabilized as a single alloy phase represent a new material system with promising properties, such as high corrosion and creep resistance, sluggish diffusion, and high temperature tensile strength. However, the mechanism of stabilization to form single phase alloys is controversial. Early studies hypothesized that a large entropy of mixing was responsible for stabilizing the single phase; more recent work has proposed that the single-phase solid solution is the result of mutual solubility of the principal elements. Here, we demonstrate the first self-consistent study of the relative importance of these two proposed mechanisms. In situ high-throughput synchrotron diffraction studies were used to monitor the stability of the single phase alloy in thin-film (Al1-x-yCuxMoy)FeNiTiVZr composition spread samples. Our results indicate that a metastable solid solution can be captured via the rapid quenching typical of physical vapor deposition processes, but upon annealing the solid-solution phase stability is primarily governed by mutual miscibility.

Self-healing catalysts: Co(3)O(4) nanorods for Fischer-Tropsch synthesis.

We combine kinetic and spectroscopic data to demonstrate the concept of a self-healing catalyst, which effectively eliminates the need for catalyst regeneration. The observed self-healing is triggered by controlling the crystallographic orientation at the catalyst surface.

Combinatorial approach to turbine bond coat discovery.

A nondestructive method for the high-throughput screening of novel bond coat materials has been developed. By using a suite of characterization techniques, including Raman spectroscopy, fluorescence spectroscopy, and X-ray diffraction, a rapid determination of thermally grown oxide phases and their protective capability over a continuous composition spread sample can be obtained. The methodology is validated with the Ni-Al system. The procedure developed in this work results in the rapid identification of bond coat composition regions in which the preferred thermally grown oxide, α-Al2O3, is nucleated thus significantly reducing the amount of phase space that needs to be explored in subsequent studies.