A5037150873- Advanced Thermodynamics and Statistical Mechanics
- Theoretical and Computational Physics
- Phase Equilibria and Thermodynamics
- Machine Learning in Materials Science
- Protein Structure and Dynamics
University of Bayreuth
2023
We argue in favour of developing a comprehensive dynamical theory for rationalizing, predicting, designing, and machine learning nonequilibrium phenomena that occur soft matter. To give guidance navigating the theoretical practical challenges lie ahead, we discuss exemplify limitations density functional (DDFT). Instead implied adiabatic sequence equilibrium states this approach provides as makeshift true time evolution, posit pending tasks systematic understanding relationships govern...
Abstract We combine power functional theory and machine learning to study non-equilibrium overdamped many-body systems of colloidal particles at the level one-body fields. first sample in steady state fields relevant for dynamics from computer simulations Brownian under influence randomly generated external A neural network is then trained with this data represent locally space formally exact mapping density velocity profiles internal force field. The used analyse superadiabatic field...
We argue in favour of developing a comprehensive dynamical theory for rationalizing, predicting, designing, and machine learning nonequilibrium phenomena that occur soft matter. To give guidance navigating the theoretical practical challenges lie ahead, we discuss exemplify limitations density functional theory. Instead implied adiabatic sequence equilibrium states this approach provides as makeshift true time evolution, posit pending tasks systematic understanding relationships govern...