Building Data-driven Models with Microstructural Images: Generalization and Interpretability
This paper explores the use of convolutional neural networks for classifying microstructure.
2017
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.
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 […]
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 […]
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. […]
2016
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 […]
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).
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 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 […]
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 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. […]
Perspective: 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 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. […]