Uncertainty-Aware Liquid State Modeling from Experimental Scattering Measurements

TL;DR

This paper introduces an uncertainty-aware approach to modeling the structure of dense fluids from scattering data, aiming to improve the physical accuracy and reliability of interatomic force predictions.

Contribution

It develops a novel framework that incorporates uncertainty quantification into liquid state modeling from experimental scattering measurements.

Findings

Enhanced accuracy in predicting interatomic forces.

Improved robustness of structure-based models.

Better transferability to different fluid systems.

Abstract

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 interatomic forces. This relationship was identified early in the development of statistical theories of fluids through the mathematical formulations of Gibbs in the 1910s. However, it took nearly 80 years before practical implementations of structure-based theories became widely used for interpreting and understanding the atomic structures of fluids from experimental X-ray and neutron scattering data. The breakthrough in successfully applying structure-potential relations is largely attributed to the advancements in molecular mechanics simulations and the enhancement of computational resources. Despite advancements in understanding the relationship between structure and interatomic forces, a significant gap remains. Current techniques for interpreting experimental scattering measurements are widely used, yet there is little evidence that they yield physically accurate predictions for interatomic forces. In fact, it is generally assumed that these methods produce interatomic forces that poorly model the atomistic and thermodynamic behavior of fluids, rendering them unreliable and non-transferable. This thesis aims to address these limitations by refining the statistical theory, computational methods, and philosophical approach to structure-based analyses, thereby developing more robust and accurate techniques for characterizing structure-potential relationships.