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...
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...
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...
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...
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...
Papers Mentioning Citrine
Beyond bulk single crystals: A data format for all materials structure–property–processing relationships
In this article, we discuss a hierarchical data structure used for storing materials data called the physical information file (PIF).
Papers By 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...
Papers Mentioning Citrine
The Materials Data Facility: Data Services to Advance Materials Science Research
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...
Papers Mentioning Citrine
Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access
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...
Papers By Citrine
Web-based machine learning models for real-time screening of thermoelectric materials properties
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...
Papers By Citrine