3D printing, or additive manufacturing, of metals uses a direct energy source, such as a laser or electron beam, to alloy powders, but has succeeded for only a few...
We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory...
In this review, we discuss current and potential future applications for materials informatics in industry. We include in this discussion not only the traditional materials and chemical industries, but...
The optimization of composition and processing to obtain materials that exhibit desirable characteristics has historically relied on a combination of scientist intuition, trial and error, and luck. We propose...
A machine-learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials. Improving these properties requires...
Building on these efforts, new tools are being developed to improve data curation, such as the Materials Data Curation System,70 Materials Commons,71 and the Citrine platform.55 Chance and Paul72 outline...
Manual attribution of crystallographic phases from high-throughput x-ray diffraction studies is an arduous task, and represents a rate-limiting step in high-throughput exploration of new materials. Here, we demonstrate a...
With increasingly strict data management requirements from funding agencies and institutions, expanding focus on the challenges of research replicability, and growing data sizes and heterogeneity, new data needs are...
Considerations are presented around the design of a materials data infrastructure including import of structured and unstructured data, storage of that data for archival and retrieval, and access to...
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