Experimental Evidence of Quantum Drude Oscillator Behavior in Liquids Revealed with Probabilistic Iterative Boltzmann Inversion

TL;DR

This paper provides the first experimental evidence of quantum Drude oscillator behavior in liquids, using machine learning-enhanced iterative Boltzmann inversion on noble gas data, and simplifies classical force fields to a single parameter.

Contribution

It introduces a novel application of probabilistic machine learning to identify quantum effects in liquids and simplifies force field models based on atomic polarizability.

Findings

Quantum Drude oscillator behavior observed in noble gases.

Classical force fields can be reduced to a single parameter.

Neutron scattering data can inform force field design.

Abstract

The first experimental evidence of quantum Drude oscillator behavior in liquids is determined using probabilistic machine learning-augmented iterative Boltzmann inversion applied to noble gas radial distribution functions. Furthermore, classical force fields for noble gases are shown to be reduced to a single parameter through simple empirical relations linked to atomic dipole polarizability. These findings highlight how neutron scattering data can inspire innovative force field design and offer insight into interatomic forces to advance molecular simulations.