Applications of Autonomous Data Collection and Active Learning

Journal Article · Tue Jun 28 00:00:00 EDT 2022 · JOM. Journal of the Minerals, Metals & Materials Society

DOI:https://doi.org/10.1007/s11837-022-05374-1· OSTI ID:1877130

Polonsky, Andrew T. ^[1]; Callahan, Patrick G. ^[2]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2. Naval Research Lab. (NRL), Washington, DC (United States)

Advances in sensors and robotics have dramatically improved the diversity of experimental approaches available to the materials community. Autonomous data collection platforms, either custom-made or commercially available, provide researchers with novel tools with which to probe materials behavior and perform advanced materials characterization. The application of novel control algorithms and active learning approaches can create much more robust experimental data, or can be used to improve the performance of existing characterization tools. Five papers within this special topic focus on experimental and computational methodologies for use in automatic data collection routines for materials characterization. From novel platforms for materials discovery to new statistical frameworks for assessing the autonomous experimentation process, these five papers highlight the diverse range of applications of automation for advancing materials science.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: Naval Research Laboratory (NRL); USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003525
• OSTI ID: 1877130
• Report Number(s): SAND2022-8162J; 707529
• Journal Information: JOM. Journal of the Minerals, Metals & Materials Society, Journal Name: JOM. Journal of the Minerals, Metals & Materials Society Journal Issue: 8 Vol. 74; ISSN 1047-4838
• Publisher: Springer
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE