Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
Traditional machine learning (ML) metrics overestimate model performance for materials discovery. We introduce (1) leave-one-cluster-out cross-validation (LOCO CV) and (2) a simple nearest-neighbor benchmark to show that model performance...
Papers By Citrine
Materials Data Infrastructure and Materials Informatics
Data-driven materials research requires two key supporting components: data infrastructure and informatics. In this chapter, we review the state of the art in materials data infrastructure, focusing in detail...
Papers By Citrine
Building Data-driven Models with Microstructural Images: Generalization and Interpretability
This paper explores the use of convolutional neural networks for classifying microstructure.
Papers By Citrine
Overcoming data scarcity with transfer learning
In this paper, we describe and compare three techniques for transfer learning: multi-task, difference, and explicit latent variable architectures.
Papers By Citrine
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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...
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...
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