Work

Metadensity Functional Theory for Classical Fluids: Extracting the Pair Potential

Year: 2025

Type: article

Abstract: The excess free energy functional of classical density functional theory depends upon the type of fluid model, specifically on the choice of (pair) potential. This functional is unknown in general and is approximated reliably only in special cases. We present a machine learning scheme for training a neural network that acts as a generic metadensity functional for truncated but otherwise arbitrary pair potentials. Automatic differentiation and neural functional calculus then yield, for one-dimensional fluids, accurate predictions for inhomogeneous states and immediate access to the pair distribution function. The approach provides a means of addressing a fundamental problem in the physics of liquids and for soft matter design: "How do we best invert structural data to obtain the pair potential?"

Source: Physical Review Letters

DOI: 10.1103/physrevlett.134.107301

Authors: Stefanie M. Kampa, Florian Sammüller, Matthias Schmidt, Robert Evans

Institutions: University of Bayreuth, University of Bristol

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,750

Funders: Leverhulme Trust, Deutsche Forschungsgemeinschaft