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.

Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets

Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional representations demanded by the rich hierarchical material structure into useful, high value, low dimensional representations. In this paper, we develop and demonstrate the merits of a data-driven approach for addressing this challenge at the atomic scale. The approach presented here is built on prior successes demonstrated for mesoscale representations of material internal structure, and involves three main steps: (i) digital representation of the material structure, (ii) extraction of a comprehensive set of structure measures using the framework of n-point spatial correlations, and (iii) identification of data-driven low dimensional measures using principal component analyses. These novel protocols, applied on an ensemble of structure datasets output from molecular dynamics (MD) simulations, have successfully classified the datasets based on several model input parameters such as the interatomic potential and the temperature used in the MD simulations.

Relationship between equilibrium fluctuations and shear-coupled motion of grain boundaries

We derive general analytical expressions relating the equilibrium fluctuations of a grain boundary to key parameters governing its motion coupled to shear deformation. We validate these expressions by molecular dynamics simulations for symmetrical tilt boundaries and demonstrate how they can be used to extract the misorientation dependence of the grain-boundary mobility. The results shed light on fundamental relationships between equilibrium and nonequilibrium grain-boundary properties and provide new means to predict those properties.

Interface Mobility from Interface Random Walk

Computational studies aimed at extracting interface mobilities require driving forces orders of magnitude higher than those occurring experimentally. We present a computational methodology that extracts the absolute interface mobility in the zero driving force limit by monitoring the one-dimensional random walk of the mean interface position along the interface normal. The method exploits a fluctuation-dissipation relation similar to the Stokes-Einstein relation, which relates the diffusion coefficient of this Brownian-like random walk to the interface mobility. Atomic-scale simulations of grain boundaries in model crystalline systems validate the theoretical predictions and highlight the profound effect of impurities. The generality of this technique, combined with its inherent spatiotemporal efficiency, should allow computational studies to effectively complement experiments in understanding interface kinetics in diverse material systems.