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 […]
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 […]
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. […]
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 […]
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 […]
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. […]
In this paper, we describe and compare three techniques for transfer learning: multi-task, difference, and explicit latent variable architectures.
This paper explores the use of convolutional neural networks for classifying microstructure.
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 […]
Metal-based additive manufacturing, or three-dimensional (3D) printing, is a potentially disruptive technology across multiple industries, including the aerospace, biomedical and automotive industries. Building up metal components layer by layer increases design freedom and manufacturing flexibility, thereby enabling complex geometries, increased product customization and shorter time to market, while eliminating traditional economy-of-scale constraints. However, currently only […]
