machine learning

Molecular dynamics is a valuable tool to probe biological processes at the atomistic level - a resolution often elusive to experiments. …

Here we present a nonparametric Bayesian framework to infer radial distribution functions with uncertainty quantification from …

Charge scaling, also denoted as the electronic continuum correction, has proven to be an efficient method of effectively including …

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.

The first experimental evidence of quantum Drude oscillator behavior in liquids is determined using probabilistic machine …

This dissertation is founded on the central notion that structural correlations in dense fluids, such as dense gases, liquids, and …

Learning interaction potentials from the structure factor is frequently seen as impractical due to accuracy constraints of neutron and …

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.

While Bayesian inference is the gold standard for uncertainty quantification and propagation, its use within physical chemistry …

Airborn imaging spectroscopy is a valuable method to characterize the distribution of plant life for ecological, agricultural, and environmental research. In this study, spectroscopy measurements of ‘Ohi’a Lehua, a keystone tree species on the Big Island of Haw’aii, were collected for tree samples at various locations across the island.