thermodynamics

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.

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 …

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

Gaussian processes (GPs) are a Bayesian regression technique with big upside for machine learning interatomic potentials. Unlike black-box methods like neural networks, GPs are interpretable, uncertainty-aware, and allow for the integration of physics-informed behavior through the use of carefully designed Bayesian priors.