Data-driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

被引:19
|
作者
De Guire, Eileen [1 ]
Bartolo, Laura [2 ]
Brindle, Ross [3 ]
Devanathan, Ram [4 ]
Dickey, Elizabeth C. [5 ]
Fessler, Justin [6 ]
French, Roger H. [7 ]
Fotheringham, Ulrich [8 ]
Harmer, Martin [9 ]
Lara-Curzio, Edgar [10 ]
Lichtner, Sarah [3 ]
Maillet, Emmanuel [11 ]
Mauro, John [12 ]
Mecklenborg, Mark [1 ]
Meredig, Bryce [13 ]
Rajan, Krishna [14 ]
Rickman, Jeffrey [9 ]
Sinnott, Susan [12 ]
Spahr, Charlie [1 ]
Suh, Changwon [3 ]
Tandia, Adama [15 ]
Ward, Logan [16 ]
Weber, Rick [17 ]
机构
[1] Amer Ceram Soc, 550 Polaris Pkwy,Ste 510, Westerville, OH 43082 USA
[2] Northwestern Univ, Northwestern Argonne Inst Sci & Engn, Evanston, IL USA
[3] Nexight Grp, Silver Spring, MD USA
[4] Pacific Northwest Natl Lab, Energy & Environm Directorate, Richland, WA 99352 USA
[5] North Carolina State Univ, Dept Mat Sci & Engn, Raleigh, NC USA
[6] IBM Watson, Arlington, VA USA
[7] Case Western Reserve Univ, Dept Mat Sci & Engn, Cleveland, OH 44106 USA
[8] SCHOTT AG, Mainz, Germany
[9] Lehigh Univ, Dept Mat Sci & Engn, Bethlehem, PA 18015 USA
[10] Oak Ridge Natl Lab, Mech Properties & Mech Grp, Oak Ridge, TN USA
[11] GE Global Res, Mat Sci & Engn, Niskayuna, NY USA
[12] Penn State Univ, Dept Mat Sci & Engn, University Pk, PA 16802 USA
[13] Citrine Informat, Redwood City, CA USA
[14] Univ Buffalo, Dept Mat Design & Innovat, Buffalo, NY USA
[15] Corning Inc, Corning, NY 14831 USA
[16] Univ Chicago, Globus Labs, Chicago, IL 60637 USA
[17] Mat Dev Inc, Arlington Hts, IL USA
基金
美国国家科学基金会;
关键词
glass; modeling; model; simulation; DESIGN; OPTIMIZATION; DISCOVERY; ANALYTICS;
D O I
10.1111/jace.16677
中图分类号
TQ174 [陶瓷工业]; TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Data-driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high-end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics to develop better functional materials more efficiently. Simultaneously, the data science community is looking for a way to access materials data resources to test and validate their advanced computational learning algorithms. To address this issue, The American Ceramic Society (ACerS) convened a Glass and Ceramic Data Science Workshop in February 2018, sponsored by the National Institute for Standards and Technology (NIST) Advanced Manufacturing Technologies (AMTech) program. The workshop brought together a select group of leaders in the data science, informatics, and glass and ceramics communities, ACerS, and Nexight Group to identify the greatest opportunities and mechanisms for facilitating increased collaboration and coordination between these communities. This article summarizes workshop discussions about the current challenges that limit interactions and collaboration between the glass and ceramic and data science communities, opportunities for a coordinated approach that leverages existing knowledge in both communities, and a clear path toward the enhanced use of data science technologies for functional glass and ceramic research and development.
引用
收藏
页码:6385 / 6406
页数:22
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