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Home / Research / Publications

Publications

Overcoming data scarcity with transfer learning

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. […]

Papers By Citrine
3D printing of high-strength aluminum alloys

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 metals. Often, large columnar grains and cracks are generated during the solidification stage. In this paper, John Martin et al. confront this problem for aerospace-grade aluminium alloys […]

Papers Mentioning Citrine
Robust FCC solute diffusion predictions from ab-initio machine learning methods

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 […]

Papers By Citrine
Industrial materials informatics: Analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain

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 […]

Papers By Citrine
High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates

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. […]

Papers By Citrine
High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

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 […]

Papers By Citrine
Role of materials data science and informatics in accelerated materials innovation

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 how to connect the wide variety of data sets and tools using a semantic web infrastructure. Kalidindi, S. R., Brough, D. B., Li, S., Cecen, A., […]

Papers Mentioning Citrine
Beyond bulk single crystals: A data format for all materials structure–property–processing relationships

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 […]

Papers By Citrine
Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

In the current implementation, SS-AutoPhase (semi-supervised AutoPhase) was used to phase map 278 diffractograms from a FeGaPd “open-data” combinatorial thin-film library.[Citrine Informatics, Fe-Ga-Pd, Citrination, http://citrination.com] Bunn, J. K., Hu, J., & Hattrick-Simpers, J. R. (2016). Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns. JOM, 68(8), 2116-2125.

Papers Mentioning Citrine
Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

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 semi-supervised machine learning technique, SS-AutoPhase, which uses a two-step approach to identify automatically phases from diffraction data. First, clustering analysis is used to select a representative […]

Papers Mentioning Citrine

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