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.

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.

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.

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.

Characterization data of an (AlFeNiTiVZr)Cr 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)Cr alloy" [1]. This data article describes the high-throughput synthesis and characterization processes of an (AlFeNiTiVZr)Cr 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.

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.

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.

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).

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.

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.

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.

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.

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 Fe–Cr–Al alloy system. Composition-spread samples were synthesized to cover the region of the phase diagram previous bulk studies have identified as forming protective oxides.

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.

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.

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.

Development of a high-throughput methodology for screening coking resistance of modified thin-film catalysts.

A high-throughput optical technique has been developed for the rapid screening of coking resistant composition-spread promoted-catalyst libraries during hydrocarbon cracking, in particular for Jet Propellant 8(JP-8) cracking. The libraries are screened by measuring changes in the catalyst's surface color due to the accumulation and burnoff of coke from the surface during JP-8 exposure and catalyst regeneration via oxygen burnoff. This rapid screening method was validated through a comparison of the coking properties of high-surface area powder cracking catalysts, and sputter deposited samples.

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.

Giant magnetostriction in annealed Co(1-x)Fe(x) thin-films.

Chemical and structural heterogeneity and the resulting interaction of coexisting phases can lead to extraordinary behaviours in oxides, as observed in piezoelectric materials at morphotropic phase boundaries and relaxor ferroelectrics. However, such phenomena are rare in metallic alloys. Here we show that, by tuning the presence of structural heterogeneity in textured Co(1-x)Fe(x) thin films, effective magnetostriction λ(eff) as large as 260 p.

Optical cell for combinatorial in situ Raman spectroscopic measurements of hydrogen storage materials at high pressures and temperatures.

An optical cell is described for high-throughput backscattering Raman spectroscopic measurements of hydrogen storage materials at pressures up to 10 MPa and temperatures up to 823 K. High throughput is obtained by employing a 60 mm diameter × 9 mm thick sapphire window, with a corresponding 50 mm diameter unobstructed optical aperture. To reproducibly seal this relatively large window to the cell body at elevated temperatures and pressures, a gold o-ring is employed.

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.