As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process–structure–property relations throughout materials science, it seems logical that models that can leverage microstructural data would be more capable of predicting property information. While there have […]

Despite increasing focus on data publication and discovery in materials science and related fields, the global view of materials data is highly sparse. This sparsity encourages training models on the union of multiple datasets, but simple unions can prove problematic as (ostensibly) equivalent properties may be measured or computed differently depending on the data source. […]

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 (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural […]

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 also other manufacturing-intensive sectors, which broadens the relevance of materials informatics to a large proportion of the economy. We describe several high-level use cases, drawing upon […]

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 methodology that can accelerate this process by fitting data-driven models to experimental data as it is collected to suggest which experiment should be performed next. […]

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 knowledge of crystal structures, which occur in three subtle variations (Heusler, inverse Heusler, and CsCl-type structures) that are difficult, and at times impossible, to distinguish by […]

Methods used in informatics require input data that are in a machine-readable, structured format. Materials data, in particular, can be exceedingly complex, so defining data formats to store any and all materials-related information is a daunting task. In this article, we discuss a hierarchical data structure used for storing materials data called the physical information […]

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 that data through programmatic and graphical interfaces. In particular, the choices around technologies used in such an infrastructure, the benefits and drawbacks of those technologies, and […]

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structuralfamilies, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT)offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. […]

The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor [J. […]