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

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.

Looking through glass: Knowledge discovery from materials science literature using natural language processing

“…Just as in all other branches of science, the materials community is afflicted by the curse of knowledge incommensurate with the available information. 1,2 THE BIGGER PICTURE Most knowledge generated through scientific enquiry in materials domain is presented in the form of unstructured data. Among the available sources such as online websites, digital data, and publications, peer-reviewed journals serve as the undisputed source of reliable information regarding materials synthesis, characterization, and properties.…”

“…The information on material science is increasingly siloed and simply too large for the efficient utilization of any one individual or group. Just as in all other branches of science, the materials community is afflicted by the curse of knowledge incommensurate with the available information 1,2 . Thus, the accessibility to the vast majority of knowledge in the literature is limited, as it: (i) is time consuming to manually read and analyze the texts and images, and (ii) requires a domain expert to understand, interpret, and summarize the information.…”

Statistical mechanical model for the formation of octahedral silicon in phosphosilicate glasses

“…9,10 As the topological constraints are a direct measure of the short-range glass structures, establishing a composition-structure model for this glass system would enable prediction of phosphosilicate glass properties directly from their composition. [11][12][13][14][15] The aim of this study is to develop such a composition-structure model. Recently, a statistical mechanics-based modeling approach has been applied to determine relative enthalpy barriers for modifiers to associate with the various network former units in binary modifier-former glasses, 16,17 and consequently to predict the compositional evolution of shortrange structural units.…”

In‐situ observation of AlN formation from Ni‐Al solution using an electromagnetic levitation technique

“…The EML technique is generally used for the measurement of thermophysical properties or investigation of the solidification process of metallic melts. In this paper, we describe the details of the in‐situ observation system and the quantitative analysis of the AlN nucleation and growth from the Ni‐Al solution using computer vision and materials data science methods . Moreover, the quality of the AlN crystal grown from the Ni‐Al solution was subsequently characterized by scanning electron microscopy, X‐ray diffractometry, and Raman spectroscopy.…”