thermodynamics

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 …

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.

Gaussian processes provide a Bayesian framework to analyze experimental scattering data while performing rigorous uncertainty quantification and propagation (UQ/P). Our team uses state-of-the-art Gaussian process approaches, including spectral and non-stationary kernel design, to design next generation scattering analysis tools.

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 …