Skip to main navigation Skip to search Skip to main content

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

  • Eileen De Guire
  • , Laura Bartolo
  • , Ross Brindle
  • , Ram Devanathan
  • , Elizabeth C. Dickey
  • , Justin Fessler
  • , Roger H. French
  • , Ulrich Fotheringham
  • , Martin Harmer
  • , Edgar Lara-Curzio
  • , Sarah Lichtner
  • , Emmanuel Maillet
  • , John Mauro
  • , Mark Mecklenborg
  • , Bryce Meredig
  • , Krishna Rajan
  • , Jeffrey Rickman
  • , Susan Sinnott
  • , Charlie Spahr
  • , Changwon Suh
  • Adama Tandia, Logan Ward, Rick Weber
  • The American Ceramic Society
  • Northwestern University
  • Nexight Group
  • Pacific Northwest National Laboratory
  • North Carolina State University
  • IBM
  • Case Western Reserve University
  • Schott AG
  • Lehigh University
  • Oak Ridge National Laboratory
  • General Electric
  • Pennsylvania State University
  • Citrine Informatics
  • Corning Incorporated
  • The University of Chicago
  • Materials Development Inc.

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)6385-6406
Number of pages22
JournalJournal of the American Ceramic Society
Volume102
Issue number11
DOIs
StatePublished - Nov 1 2019

Keywords

  • glass
  • modeling/model
  • simulation

Fingerprint

Dive into the research topics of 'Data-driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities'. Together they form a unique fingerprint.

Cite this