neutron scattering

Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data
Here we present a nonparametric Bayesian framework to infer radial distribution functions with uncertainty quantification from …
Harry W. Sullivan, Matej Cervenka, Brennon L. Shanks, Michael P. Hoepfner
PDF Cite Code DOI
Charge Scaling Force Field for Biologically Relevant Ions Utilizing a Global Optimization Method
Charge scaling, also denoted as the electronic continuum correction, has proven to be an efficient method of effectively including …
Shujie Fan, Philip E. Mason, Victor Cruces Chamorro, Brennon L. Shanks, Hector Martinez-Seara, Pavel Jungwirth
PDF Cite Code DOI
Cation-pi Interactions in Biomolecular Simulations by Neutron Scattering and Molecular Dynamics: Case Study of the Tetramethylammonium Cation
Cation-$\pi$ interactions involving the tetramethylammonium motif are prevalent in biological systems, playing crucial roles in …
Matej Cervenka, Brennon L. Shanks, Philip E. Mason, Pavel Jungwirth
PDF Cite Code DOI
Experimental Evidence of Quantum Drude Oscillator Behavior in Liquids Revealed with Probabilistic Iterative Boltzmann Inversion
The first experimental evidence of quantum Drude oscillator behavior in liquids is determined using probabilistic machine …
Brennon L. Shanks, Harry W. Sullivan, Pavel Jungwirth, Michael P. Hoepfner
PDF Cite Code DOI
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
PDF Cite DOI
Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data
Learning interaction potentials from the structure factor is frequently seen as impractical due to accuracy constraints of neutron and …
Brennon L. Shanks, Harry W. Sullivan, Michael P. Hoepfner
PDF Cite Code DOI
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
PDF Cite Code DOI
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
PDF Cite Code DOI