SMART Algorithm Makes Beamline Data Collection Smarter

Synthetic test function in two dimensions that is continuous and also smooth. The "data deluge" in scientific research stems in large part from the growing sophistication of experimental instrumentation and optimizing tools -- often using machine- and deep-learning methods -- to analyze increasingly large data sets. But what is equally important for improving scientific productivity is the optimization of data collection -- aka "data taking" -- methods. Toward this end, Marcus Noack, a postdoctoral scholar at Lawrence Berkeley National Laboratory in the Center for Advanced Mathematics for Energy Research Applications (CAMERA), and James Sethian, director of CAMERA and Professor of Mathematics at UC Berkeley, have been working with beamline scientists at Brookhaven National Laboratory to develop and test SMART (Surrogate Model Autonomous Experiment), a mathematical method that enables autonomous experimental decision making without human interaction. A paper describing SMART and its application in experiments at Brookhaven's National Synchrotron Light Source II (NSLS-II) are described in "A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering," published in Scientific Reports.