thermodynamics

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.

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.

The thermodynamic requirement that the second derivative of energy be positive gives rise to some interesting results on thermodynamic system stability. To see this, let’s first construct an intrinsic system and a complimentary subsystem from a composite, isolated system.

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