12 citations · 24 across the 8 of their papers we have counts for
8 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…
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…
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…
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…
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…
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…
Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models
B. L. Shanks, H. W. Sullivan, A. R. Shazed +1
While Bayesian inference is the gold standard for uncertainty quantification and propagation, its use within physical chemistry encounters formidable computational barriers. These…
Uncertainty quantification design principles for machine learning interatomic potentials: lessons learned from hierarchical Bayesian inference
Albert P. Bartók, Brennon L. Shanks, James. R. Kermode +1
Reliable uncertainty quantification (UQ) remains a major challenge for machine learning interatomic potentials (MLIPs). Here, we benchmark UQ strategies for message passing neural…