Brennon Shanks
Brennon Shanks
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Force Field Design with Bayesian Learning
Bayesic Force Fields (BFF) is an open source, Bayesian force field learning tool aimed at addressing transferability and robustness issues with existing biomolecular force fields. The code performs efficient Bayesian inference with physically motivated priors on Coulombic, bonded, and non-bonded terms.
Brennon L. Shanks
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
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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
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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
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
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Gaussian Processes for Molecular Modeling
Gaussian processes can model complex experimental data and interatomic interactions while enabling rigorous uncertainty quantification and propagation within a Bayesian framework. We apply state-of-the-art GP methods, including local [1], spectral, and non-stationary kernel design [2], to develop next-generation molecular simulation tools.
Brennon L. Shanks
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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The Henderson Inverse Theorem
The Henderson Inverse Theorem is an important result on the relationship between the radial distribution function and pairwise additive potential in a statistical ensemble. This theorem is the basis for the structure-optimized potential refinement algorithm and provides a variational solution to the statistical mechanical inverse problem.
Brennon L. Shanks
Last updated on Nov 1, 2025
4 min read
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Kirkwood-Buff Theory of Fluid Thermodynamics
The Kirkwood-Buff solution theory was presented in a landmark paper in 1951. The theory relates particle number fluctuations in the grand canonical ensemble to integrals of the radial distribution function. In this short introduction to the topic, we will introduce the statistical mechanics required to understand Kirkwood-Buff solution theory.
Brennon L. Shanks
Last updated on Jun 13, 2023
7 min read
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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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