Bayesian inference of solid-liquid interfacial properties out of equilibrium

Area of Science:

• Materials Science
• Thermodynamics
• Computational Materials Science

Background:

• Solid-liquid interfacial properties are key to controlling solidification microstructures in metals.
• Detailed characterization of these properties out of equilibrium remains challenging.

Purpose of the Study:

• To develop a novel approach for simultaneously estimating all interfacial properties of a pure metal below its melting point.
• To apply Bayesian inference theory for accurate property determination.

Main Methods:

• Integration of molecular dynamics (MD) and phase-field (PF) simulations.
• Utilizing an ensemble Kalman filter, a data assimilation technique, for parameter estimation.
• Focusing on pure iron (Fe) as a model system.

Main Results:

• Successfully estimated solid-liquid interfacial energy, interfacial mobility, and anisotropy parameters for pure Fe.
• Computed and analyzed the temperature dependencies of these interfacial parameters.
• Demonstrated a multiscale approach integrating atomistic and microstructural simulations.

Conclusions:

• The proposed data-driven, multiscale approach effectively estimates key interfacial properties.
• This methodology offers significant potential for advancing materials engineering and designing new materials.
• Provides a framework for understanding and controlling solidification processes.