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State-dependent diffusion coefficients and free energies for nucleation processes from Bayesian trajectory analysis.

Max Innerbichler¹, Georg Menzl¹, Christoph Dellago^1,2

¹Faculty of Physics and Center for Computational Materials Science, University of Vienna, Vienna, Austria.

Molecular Physics

|October 20, 2018

Summary

We developed a Bayesian inference method to calculate nucleation barriers and diffusion coefficients from molecular dynamics simulations. This approach accurately models bubble nucleation in water under negative pressure.

Keywords:

Bayesian inference cavitation classical nucleation theory diffusion nucleation

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Area of Science:

Thermodynamics
Statistical Mechanics
Computational Chemistry

Background:

Nucleation rates depend on nucleation barriers and diffusion coefficients.
Estimating these quantities from simulations is challenging.

Purpose of the Study:

To develop a Bayesian inference method for extracting free energy profiles and diffusion coefficients from molecular dynamics (MD) trajectories.
To apply this method to study vapor bubble nucleation in supercooled water under negative pressure.

Main Methods:

Bayesian inference algorithm for Markovian dynamics.
Molecular dynamics simulations.
Using largest bubble volume as a reaction coordinate.

Main Results:

Simultaneously extracted free energy profiles and diffusion coefficients.
Diffusivity showed a linear dependence on bubble volume and pressure.
Diffusivity is primarily influenced by liquid viscosity, consistent with Rayleigh-Plesset theory.

Conclusions:

The developed Bayesian method is broadly applicable to nucleation processes.
It provides crucial data for estimating nucleation rates in classical nucleation theory.
The findings offer insights into bubble nucleation dynamics in liquids.