Revealing the atomic ordering of binary intermetallics using in situ heating techniques at multilength scales.

Ordered intermetallic nanoparticles are promising electrocatalysts with enhanced activity and durability for the oxygen-reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs). The ordered phase is generally identified based on the existence of superlattice ordering peaks in powder X-ray diffraction (PXRD). However, after employing a widely used postsynthesis annealing treatment, we have found that claims of "ordered" catalysts were possibly/likely mixed phases of ordered intermetallics and disordered solid solutions. Here, we employed in situ heating, synchrotron-based, X-ray diffraction to quantitatively investigate the impact of a variety of annealing conditions on the degree of ordering of large ensembles of Pt3Co nanoparticles. Monte Carlo simulations suggest that Pt3Co nanoparticles have a lower order-disorder phase transition (ODPT) temperature relative to the bulk counterpart. Furthermore, we employed microscopic-level in situ heating electron microscopy to directly visualize the morphological changes and the formation of both fully and partially ordered nanoparticles at the atomic scale. In general, a higher degree of ordering leads to more active and durable electrocatalysts. The annealed Pt3Co/C with an optimal degree of ordering exhibited significantly enhanced durability, relative to the disordered counterpart, in practical membrane electrode assembly (MEA) measurements. The results highlight the importance of understanding the annealing process to maximize the degree of ordering in intermetallics to optimize electrocatalytic activity.

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.

Demonstration of a laser powder bed fusion combinatorial sample for high-throughput microstructure and indentation characterization.

High-throughput experiments that use combinatorial samples with rapid measurements can be used to provide process-structure-property information at reduced time, cost, and effort. Developing these tools and methods is essential in additive manufacturing where new process-structure-property information is required on a frequent basis as advances are made in feedstock materials, additive machines, and post-processing. Here we demonstrate the design and use of combinatorial samples produced on a commercial laser powder bed fusion system to study 60 distinct process conditions of nickel superalloy 625: five laser powers and four laser scan speeds in three different conditions. Combinatorial samples were characterized using optical and electron microscopy, x-ray diffraction, and indentation to estimate the porosity, grain size, crystallographic texture, secondary phase precipitation, and hardness. Indentation and porosity results were compared against a regular sample. The smaller-sized regions (3 mm × 4 mm) in the combinatorial sample have a lower hardness compared to a larger regular sample (20 mm × 20 mm) with similar porosity (< 0.03 %). Despite this difference, meaningful trends were identified with the combinatorial sample for grain size, crystallographic texture, and porosity versus laser power and scan speed as well as trends with hardness versus stress-relief condition.

The effect of ALD-grown Al2O3 on the refractive index sensitivity of CVD gold-coated optical fiber sensors.

The combined effect of nanoscale dielectric and metallic layers prepared by atomic layer deposition (ALD) and chemical vapor deposition (CVD) on the refractometric properties of tilted optical fiber Bragg gratings (TFBG) is studied. A high index intermediate layer made up of either 50 nm or 100 nm layers of Al2O3 (refractive index near 1.62) was deposited by ALD and followed by thin gold layers (30-65 nm) deposited from a known single-source gold (I) iminopyrrolidinate CVD precursor. The fabricated devices were immersed in different surrounding refractive indices (SRI) and the spectral transmission response of the TFBGs was measured. Preliminary results indicate that the addition of the dielectric Al2O3 pre-coating enhances the SRI sensitivity by up to 75% but this enhancement is highly dependent on the polarization and dielectric thickness. In fact, the sensitivity decreases by up to 50% for certain cases. These effects are discussed with support from TFBG simulations and models, by quantifying the penetration of the evanescently coupled light out of the fiber through the various coating layers. Additional characterization studies have been carried out on these samples to further correlate the optical behaviour of the coated TFBGs with the physical properties of the gold and Al2O3 layers, using atomic force microscopy x-ray photoelectron spectroscopy and an ensemble of other optical and x-ray absorption spectroscopy techniques. The purity, roughness, and morphology of gold thin films deposited by CVD onto the dielectric-TFBG surface are also provided.

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.