Advances in Kriging-Based Autonomous X-Ray Scattering Experiments
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Brookhaven National Lab. (BNL), Upton, NY (United States)
Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1603612
- Journal Information:
- Scientific Reports, Vol. 10, Issue 1; ISSN 2045-2322
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels
|
journal | October 2020 |
Similar Records
Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels
An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling