3 papers
cond-mat.soft2025
Learning the bulk and interfacial physics of liquid-liquid phase separation with neural density functionals
Silas Robitschko, Florian Sammüller, Matthias Schmidt +1
We use simulation-based supervised machine learning and classical density functional theory to investigate bulk and interfacial phenomena associated with phase coexistence in binar…
cond-mat.soft2025
Determining the chemical potential via universal density functional learning
Florian Sammüller, Matthias Schmidt
We demonstrate that the machine learning of density functionals allows one to determine simultaneously the equilibrium chemical potential across simulation datasets of inhomogeneou…
cond-mat.stat-mech2025
Dynamical gauge invariance of statistical mechanics
Johanna Müller, Florian Sammüller, Matthias Schmidt
We investigate gauge invariance against phase space shifting in nonequilibrium systems, as represented by time-dependent many-body Hamiltonians that drive an initial ensemble out o…