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 […]
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 […]
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 […]
In this article, we discuss a hierarchical data structure used for storing materials data called the physical information file (PIF).
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. […]
Universal access to abundant scientific data, and the software to analyze the data at scale, could fundamentally transform the field of materials science. Today, the materials community faces serious challenges to bringing about this data-accelerated research paradigm, including diversity of research areas within materials, lack of data standards, and missing incentives for sharing, among others. […]
The process of taking a new material from invention to deployment can take 20 years or more. Since the announcement of the Materials Genome Initiative in 2011, new attention has been paid to accelerating this timeframe to address key challenges in industries from energy, to biomedical materials, to catalysis, to polymers, particularly in the development […]
We present an overview and preliminary analysis of computed thermoelectric properties for more than 48 000 inorganic compounds from the Materials Project (MP). We compare our calculations with available experimental data to evaluate the accuracy of different approximations in predicting thermoelectric properties. We observe fair agreement between experiment and computation for the maximum Seebeck coefficient determined […]
In this article we provide an overview of data mining, informatics, and machine learning approaches for thermoelectrics.
