Materials Data Science: Current Status and Future Outlook

Kalidindi, Surya R.; Graef, Marc De

doi:10.1146/annurev-matsci-070214-020844

Cited by 213 publications

(129 citation statements)

References 63 publications

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“…25,26,[68][69][70][71][72][73][74] In particular, AM will benefit from the ICME goal of enabling optimization of materials, manufacturing processes, and component design long before components are fabricated by integrating computational processes involved into a holistic system. Developing and implementing such a system will enable a more efficient qualification process using big data science approaches.…”

Section: Microstructure Informatics Modeling and Simulation-an Icme mentioning

confidence: 99%

“…Developing and implementing such a system will enable a more efficient qualification process using big data science approaches. 71,72 As a demonstration of the practical implementation of an ICME approach, the team involved in this effort integrated efforts from academia, OEMs, and a small business all working on various projects with different funding sources. However, all members recognized the value of collaboration and integration of results obtained using various tools.…”

Section: Microstructure Informatics Modeling and Simulation-an Icme mentioning

confidence: 99%

See 1 more Smart Citation

Overview of Materials Qualification Needs for Metal Additive Manufacturing

Seifi

Salem

Beuth

et al. 2016

JOM

500

183

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This overview highlights some of the key aspects regarding materials qualification needs across the additive manufacturing (AM) spectrum. AM technology has experienced considerable publicity and growth in the past few years with many successful insertions for non-mission-critical applications. However, to meet the full potential that AM has to offer, especially for flightcritical components (e.g., rotating parts, fracture-critical parts, etc.), qualification and certification efforts are necessary. While development of qualification standards will address some of these needs, this overview outlines some of the other key areas that will need to be considered in the qualification path, including various process-, microstructure-, and fracture-modeling activities in addition to integrating these with lifing activities targeting specific components. Ongoing work in the Advanced Manufacturing and Mechanical Reliability Center at Case Western Reserve University is focusing on fracture and fatigue testing to rapidly assess critical mechanical properties of some titanium alloys before and after post-processing, in addition to conducting nondestructive testing/evaluation using micro-computerized tomography at General Electric. Process mapping studies are being conducted at Carnegie Mellon University while large area microstructure characterization and informatics (EBSD and BSE) analyses are being conducted at Materials Resources LLC to enable future integration of these efforts via an Integrated Computational Materials Engineering approach to AM. Possible future pathways for materials qualification are provided.

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Section: Microstructure Informatics Modeling and Simulation-an Icme mentioning

confidence: 99%

Section: Microstructure Informatics Modeling and Simulation-an Icme mentioning

confidence: 99%

Overview of Materials Qualification Needs for Metal Additive Manufacturing

Seifi

Salem

Beuth

et al. 2016

JOM

500

183

View full text Add to dashboard Cite

show abstract

“…[132][133][134][135][136][137][138][139] Statistical analysis, interactive visualization, and machine learning allow for new and insightful ways of understanding materials behavior and guiding research efforts. The experimental and computed data in this study, provided in full in the Supporting Information, afford opportunities for this type of analysis.…”

Section: Resultsmentioning

confidence: 99%

A Simple Computational Proxy for Screening Magnetocaloric Compounds

et al. 2017

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Alternating cycles of isothermal magnetization and adiabatic demagnetization applied to a magnetocaloric material can drive refrigeration in very much the same manner as cycles of gas compression and expansion. The material property of interest in finding candidate magnetocaloric materials is their gravimetric entropy change upon application of a magnetic field under isothermal conditions. There is, however, no general method of screening materials for such an entropy change without actually carrying out the relevant, time-and effort-intensive magnetic measurements. Here we propose a simple computational proxy based on carrying out non-magnetic and magnetic density functional theory calculations on magnetic materials. This proxy, which we refer to as the magnetic deformation Σ M , is a measure of how much the unit cell deforms when comparing the relaxed structures with and without the inclusion of spin polarization. Σ M appears to correlate very well with experimentally measured magnetic entropy change values. The proxy has been tested against 33 known ferromagnetic materials, including nine materials newly measured for this study. It has then been used to screen 134 ferromagnetic materials for which the magnetic entropyhas not yet been reported, identifying 30 compounds as being promising for further study. As a demonstration of the effectiveness of our approach, we have prepared one of these compounds and measured its isothermal entropy change. MnCoP, with T C = 575 K, shows a maximum ∆S M = −6.0 J kg −1 K −1 for an applied field of H = 5 T.2

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“…The benefits of machine learning for accelerated materials data analysis have already been realized, with numerous studies showing the great potential for research and discovery. [199][200][201] These studies include a wide range of materials analysis challenges including crystal structure [202][203][204] and phase diagram 130,[205][206][207] determination, materials property predictions, 208,209 micrograph analysis, 210,211 development of interatomic potentials [212][213][214] and energy functionals 215 to improve materials simulations, and on-the-fly data analysis of high-throughput experiments. 216 …”

Section: Informaticsmentioning

confidence: 99%

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

Green

Choi

Hattrick‐Simpers

et al. 2017

Applied Physics Reviews

265

173

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The Materials Genome Initiative, a national effort to introduce new materials into the market faster and at lower cost, has made significant progress in computational simulation and modeling of materials. To build on this progress, a large amount of experimental data for validating these models, and informing more sophisticated ones, will be required. High-throughput experimentation generates large volumes of experimental data using combinatorial materials synthesis and rapid measurement techniques, making it an ideal experimental complement to bring the Materials Genome Initiative vision to fruition. This paper reviews the state-of-the-art results, opportunities, and challenges in high-throughput experimentation for materials design. A major conclusion is that an effort to deploy a federated network of high-throughput experimental (synthesis and characterization) tools, which are integrated with a modern materials data infrastructure, is needed.

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Materials Data Science: Current Status and Future Outlook

Cited by 213 publications

References 63 publications

Overview of Materials Qualification Needs for Metal Additive Manufacturing

Overview of Materials Qualification Needs for Metal Additive Manufacturing

A Simple Computational Proxy for Screening Magnetocaloric Compounds

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

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