Abstract
Neutron reflectometry has long been a powerful tool to study the interfacial properties of energy materials. Recently, time-resolved neutron reflectometry has been used to better understand transient phenomena in electrochemical systems. Those measurements often comprise a large number of reflectivity curves acquired over a narrow q range, with each individual curve having lower information content compared to a typical steady-state measurement. In this work, we present an approach that leverages existing reinforcement learning tools to model time-resolved data to extract the time evolution of structure parameters. By mapping the reflectivity curves taken at different times as individual states, we use the Soft Actor-Critic algorithm to optimize the time series of structure parameters that best represent the evolution of an electrochemical system. We show that this approach constitutes an elegant solution to the modeling of time-resolved neutron reflectometry data.
| Original language | English |
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| Pages (from-to) | 4444-4450 |
| Number of pages | 7 |
| Journal | Journal of Physical Chemistry Letters |
| Volume | 15 |
| Issue number | 16 |
| DOIs | |
| State | Published - Apr 25 2024 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://www.energy.gov/doe-public-access-plan ). The authors thank Dr. Erik Watkins for the fruitful discussions. M.D. would like to thank Randy R. Doucet for sharing his thoughts on reinforcement learning. A portion of this research used resources at the Spallation Neutron Source (SNS), a Department of Energy (DOE) Office of Science User Facility operated by Oak Ridge National Laboratory. Neutron reflectometry measurements were carried out on the Liquids Reflectometer at the SNS, which is sponsored by the Scientific User Facilities Division, Office of Basic Energy Sciences, DOE. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Separations Science program, under Award DE-SC0021409.