Assessing the Frontier: Active Learning, Model Accuracy, and Multi objective Materials Discovery and Optimization

Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates—active learning. However, standard global error metrics for model quality are not predictive of discovery performance, and can be misleading. We introduce the notion of Pareto shell error to help judge the suitability of a model for proposing material candidates. Further, through […]

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Machine Learning for Alloy Composition and Process Optimization

The drive for greater efficiency in turbomachinery has led to increasingly stringent specifications for the materials used. Current methods for optimizing alloy composition and processing to meet these requirements typically rely on a combination of expert judgment and trial and error. Machine learning offers an alternative approach that leverages data resources to significantly accelerate the […]

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Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

In general, data-driven approaches aim to identify objectively (relying largely on the available data) the embedded correlations among selected inputs and outputs needed to study or model a given phenomenon. Data-driven approaches are particularly advantageous when a full, physics-based understanding of relevant phenomena is lacking, and also when gathering new experimental data is particularly slow […]

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The 2019 Materials by Design Roadmap

Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. Increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. […]

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Strategies for accelerating the adoption of materials informatics

Ongoing, rapid innovations in fields ranging from microelectronics, aerospace, and automotive to defense, energy, and health demand new advanced materials at even greater rates and lower costs. Traditional materials R&D methods offer few paths to achieve both outcomes simultaneously. Materials informatics, while a nascent field, offers such a promise through screening, growing databases of materials […]

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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 in discovery applications strongly depends on the problem, data sampling, and extrapolation. Our results suggest that ML-guided iterative experimentation may outperform standard high-throughput screening for discovering […]

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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 on four infrastructure projects spanning academia, government, and industry. We also discuss data standards as an enabling step on the path to community-scale materials data infrastructure. […]

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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
Building Data-driven Models with Microstructural Images: Generalization and Interpretability

This paper explores the use of convolutional neural networks for classifying microstructure.

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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 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 […]

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