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DOE OSTI · 2500987

Implicit neural representations for experimental steering of advanced experiments

Chen, Zhantao [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)] (ORCID:0000000319543868)·Petsch, Alexander N. [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)]·Ji, Zhurun [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)]·Chitturi, Sathya R. [Stanford University, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:0000000202988476)·Peng, Cheng [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)]·Jia, Chunjing [University of Florida, Gainesville, FL (United States)]·Kolesnikov, Alexander I. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000319404649)·Thayer, Jana B. [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)]·Turner, Joshua J. [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)]
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Abstract

Scattering measurements using electrons, neutrons, or photons are essential for obtaining microscopic insights into materials. However, limited facility availability and high-dimensional scattering data necessitate more efficient experimental steering techniques. Here, we report a machine learning method that guides scattering data collection and facilitates real-time estimation of model parameters, given a reliable forward model to simulate experimental signals. We employ implicit neural representations as efficient surrogates that link model parameters with simulated spectroscopies. This enables a Bayesian optimal experimental design framework to estimate the probability distributions of parameters from high-dimensional scattering data. We demonstrate the proposed method using inelastic neutron scattering with simulated and real experimental data, highlighting the method’s ability to provide real-time parameter estimation with quantified uncertainties and to deliver informed experimental guidance that reduces experimental time while maximizing scientific output. This approach paves the way for accelerated discoveries in condensed matter through scattering measurements.

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36 MATERIALS SCIENCEexperimental designimplicit neural representationmachine learningparameter estimationscattering experiments

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Chen, Zhantao [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)] (ORCID:0000000319543868), Petsch, Alexander N. [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)], Ji, Zhurun [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)], Chitturi, Sathya R. [Stanford University, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)] (ORCID:0000000202988476), Peng, Cheng [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)], Jia, Chunjing [University of Florida, Gainesville, FL (United States)], Kolesnikov, Alexander I. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000319404649), Thayer, Jana B. [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)], Turner, Joshua J. [SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Stanford University, CA (United States)]. 2024-12-16. Implicit neural representations for experimental steering of advanced experiments. https://doi.org/10.1016/j.xcrp.2024.102333

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