machine learning

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

The inverse problem of statistical mechanics is an unsolved, century-old challenge to learn classical pair potentials directly from …

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

Structure-optimized potential refinement (SOPR) is a machine learning assisted iterative Boltzmann inversion method designed to predict accurate and transferable interaction potentials from a provided set of site-site partial radial distribution functions.

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.

Structure and self-assembly are complex, emergent properties of matter that are often misrepresented by existing molecular simulation models. In this project, we use Bayesian optimization, an accurate and robust statistical method, to optimize novel force fields based on experimental neutron/X-ray diffraction data to better model the structural behavior of liquid state systems.

Neutron/X-ray diffraction measurements can be utilized to quantify many-body interactions at the same length scale as the interatomic interactions. We use a combination of electron structure theory and structure-optimized potential refinement to directly quantify the influence of third- and higher-order effects for real fluid ensembles.

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, …

Deriving transferable pair potentials from experimental neutron and X-ray scattering measurements has been a longstanding challenge in …