Neutron Scattering

Uncertainty-Aware Liquid State Modeling from Experimental Scattering Measurements
This dissertation is founded on the central notion that structural correlations in dense fluids, such as dense gases, liquids, and …
Brennon L. Shanks
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Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data
The inverse problem of statistical mechanics is an unsolved, century-old challenge to learn classical pair potentials directly from …
Brennon L. Shanks, Harry W. Sullivan, Michael P. Hoepfner
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Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models
While Bayesian inference is the gold standard for uncertainty quantification and propagation, its use within physical chemistry …
Brennon L. Shanks, Harry W. Sullivan, Abdur R. Shazed, Michael P. Hoepfner
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Transferable Force Fields from Experimental Scattering Data with Machine Learning Assisted Structure Refinement
Deriving transferable pair potentials from experimental neutron and X-ray scattering measurements has been a longstanding challenge in …
Brennon L. Shanks, J. J. Potoff, M. P. Hoepfner
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Transferable force field development with structure-optimized potential refinement
We present an iterative predictor-corrector technique designed to derive transferable interatomic potentials from experimental X-ray or …
Jan 25, 2022 — Jan 28, 2022 Lausanne, Switzerland (remote)
Brennon L. Shanks