6 papers
Uncertainty Quantification for Free Energy Calculations by Generalized Hierarchical Bayesian Inference
Martin Skorna, Adam Gottfried, Zuzana Janackova +3
Free energy calculations are routinely used to study molecular processes inaccessible to unbiased molecular dynamics, but their utility ultimately depends on knowing when and how m…
Physics-Informed Gaussian Process Inference of Liquid Structure from Scattering Data
Harry W. Sullivan, Brennon L. Shanks, Matej Cervenka +1
We present a nonparametric Bayesian framework to infer radial distribution functions from experimental scattering measurements with uncertainty quantification using non-stationary…
Bayesian learning for accurate and robust biomolecular force fields
Vojtech Kostal, Brennon L. Shanks, Pavel Jungwirth +1
Molecular dynamics is a valuable tool to probe biological processes at the atomistic level - a resolution often elusive to experiments. However, the credibility of molecular models…
Uncertainty-Aware Liquid State Modeling from Experimental Scattering Measurements
Brennon L. Shanks
This dissertation is founded on the central notion that structural correlations in dense fluids, such as dense gases, liquids, and glasses, are directly related to fundamental inte…
Experimental Evidence of Quantum Drude Oscillator Behavior in Liquids Revealed with Probabilistic Iterative Boltzmann Inversion
Brennon L. Shanks, Harry W. Sullivan, Pavel Jungwirth +1
The first experimental evidence of quantum Drude oscillator behavior in liquids is determined using probabilistic machine learning-augmented iterative Boltzmann inversion applied t…
Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data
Brennon L. Shanks, Harry W. Sullivan, Michael P. Hoepfner
The inverse problem of statistical mechanics is an unsolved, century-old challenge to learn classical pair potentials directly from experimental scattering data. This problem was e…