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Title: A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering

Abstract

Modern scientific instruments are gathering data at ever-increasing rates, leading to an exponential increase in the size of data sets. Taking full advantage of these acquisition rates will require corresponding advancements in the speed and efficiency of data analytics and experimental control. A significant step forward would come from automatic decision-making methods that enable scientific instruments to autonomously explore scientific problems—that is, to intelligently explore parameter spaces without human intervention, selecting high-value measurements to perform based on the continually growing experimental data set. In this work, we develop such an autonomous decision-making algorithm that is physics-agnostic, generalizable, and operates in an abstract multi-dimensional parameter space. Our approach relies on constructing a surrogate model that fits and interpolates the available experimental data, and is continuously refined as more data is gathered. The distribution and correlation of the data is used to generate a corresponding uncertainty across the surrogate model. By suggesting follow-up measurements in regions of greatest uncertainty, the algorithm maximally increases knowledge with each added measurement. This procedure is applied repeatedly, with the algorithm iteratively reducing model error and thus efficiently sampling the parameter space with each new measurement that it requests. We confirm the method using synthetic data, demonstratingmore » that it converges to faithful replica of test functions more rapidly than competing methods, and demonstrate the viability of the approach in an experimental context by using it to direct autonomous small-angle (SAXS) and grazing-incidence small-angle (GISAXS) x-ray scattering experiments.« less

Authors:
 [1]; ORCiD logo [2];  [2]; ORCiD logo [2];  [2];  [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21). Scientific Discovery through Advanced Computing (SciDAC)
OSTI Identifier:
1558244
Report Number(s):
BNL-211994-2019-JAAM
Journal ID: ISSN 2045-2322
Grant/Contract Number:  
SC0012704; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Noack, Marcus M., Yager, Kevin G., Fukuto, Masafumi, Doerk, Gregory S., Li, Ruipeng, and Sethian, James A. A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering. United States: N. p., 2019. Web. doi:10.1038/s41598-019-48114-3.
Noack, Marcus M., Yager, Kevin G., Fukuto, Masafumi, Doerk, Gregory S., Li, Ruipeng, & Sethian, James A. A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering. United States. doi:10.1038/s41598-019-48114-3.
Noack, Marcus M., Yager, Kevin G., Fukuto, Masafumi, Doerk, Gregory S., Li, Ruipeng, and Sethian, James A. Wed . "A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering". United States. doi:10.1038/s41598-019-48114-3. https://www.osti.gov/servlets/purl/1558244.
@article{osti_1558244,
title = {A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering},
author = {Noack, Marcus M. and Yager, Kevin G. and Fukuto, Masafumi and Doerk, Gregory S. and Li, Ruipeng and Sethian, James A.},
abstractNote = {Modern scientific instruments are gathering data at ever-increasing rates, leading to an exponential increase in the size of data sets. Taking full advantage of these acquisition rates will require corresponding advancements in the speed and efficiency of data analytics and experimental control. A significant step forward would come from automatic decision-making methods that enable scientific instruments to autonomously explore scientific problems—that is, to intelligently explore parameter spaces without human intervention, selecting high-value measurements to perform based on the continually growing experimental data set. In this work, we develop such an autonomous decision-making algorithm that is physics-agnostic, generalizable, and operates in an abstract multi-dimensional parameter space. Our approach relies on constructing a surrogate model that fits and interpolates the available experimental data, and is continuously refined as more data is gathered. The distribution and correlation of the data is used to generate a corresponding uncertainty across the surrogate model. By suggesting follow-up measurements in regions of greatest uncertainty, the algorithm maximally increases knowledge with each added measurement. This procedure is applied repeatedly, with the algorithm iteratively reducing model error and thus efficiently sampling the parameter space with each new measurement that it requests. We confirm the method using synthetic data, demonstrating that it converges to faithful replica of test functions more rapidly than competing methods, and demonstrate the viability of the approach in an experimental context by using it to direct autonomous small-angle (SAXS) and grazing-incidence small-angle (GISAXS) x-ray scattering experiments.},
doi = {10.1038/s41598-019-48114-3},
journal = {Scientific Reports},
number = 1,
volume = 9,
place = {United States},
year = {2019},
month = {8}
}

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