Perspective: New directions in dynamical density functional theory

Diffusion and Interaction Effects On Molecular Release Kinetics From Collapsed Microgels

The efficient transport of small molecules through dense hydrogel networks is crucial for various applications, including drug delivery, biosensing, catalysis, nanofiltration, water purification, and desalination. In dense polymer matrices, such as collapsed microgels, molecular transport follows the solution-diffusion principle: Molecules dissolve in the polymeric matrix and subsequently diffuse due to a concentration gradient. Employing dynamical density functional theory (DDFT), we investigate the nonequilibrium release kinetics of nonionic subnanometer-sized molecules from a microgel particle, using parameters derived from prior molecular simulations of a thermoresponsive hydrogel. The kinetics is primarily governed by the microgel radius and two intensive parameters: the diffusion coefficient and solvation free energy of the molecule. Our results reveal two limiting regimes: a diffusion-limited regime for large, slowly diffusing, and poorly soluble molecules within the hydrogel; and a reaction-limited regime for small, rapidly diffusing, and highly soluble molecules. These principles allow us to derive an analytical equation for release time, demonstrating excellent quantitative agreement with the DDFT results-a valuable and straightforward tool for predicting release kinetics from microgels.

Tuning nonequilibrium colloidal structure in external fields by density-dependent state switching

Biological cells have the ability to switch internal states depending on the density of other cells in their local environment, referred to as "quorum sensing." The latter can be utilized to control collective structuring, such as in biofilm formation. In this work, we study a simple quorum sensing model of ideal (noninteracting) colloids with a switchable internal degree of freedom in the presence of external potentials. The colloids have two possible discrete states, in which they are affected differently by the external field, and switch with rates dependent on the local density in their environment. We study this model with reactive Brownian dynamics simulations, as well as with an appropriate reaction-diffusion theory. We find remarkable structuring in the system controlled by the density-mediated interactions between the ideal colloids. We report results of different functional forms for the rate dependence and quantify the influence of their parameters, in particular, discuss the role of the spatiotemporal sensing range, i.e., how the resulting structures depend on how the environmental information is "measured" by the colloids. Especially in the case of a rate function with sigmoidal dependence on local density, i.e., requiring a threshold density for switching, we observe significant correlation effects in the density profiles which are tuneable by the sensing ranges but also sensitive to noise and fluctuations. Hence, our model gives some basic insights into the nonequilibrium structuring mediated by simple quorum sensing protocols.

Stochastic kinetic theory applied to coarse-grained polymer model

A stochastic field theory approach is applied to a coarse-grained polymer model that will enable studies of polymer behavior under non-equilibrium conditions. This article is focused on the validation of the new model in comparison with explicit Langevin equation simulations under conditions with analytical solutions. The polymers are modeled as Hookean dumbbells in one dimension, without including hydrodynamic interactions and polymer-polymer interactions. Stochastic moment equations are derived from full field theory. The accuracy of the field theory and moment equations is quantified using autocorrelation functions. The full field theory is only accurate for a large number of polymers due to keeping track of rare occurrences of polymers with a large stretch. The moment equations do not have this error because they do not explicitly track these configurations. The accuracy of both methods depends on the spatial degree of discretization. The timescale of decorrelation over length scales bigger than the spatial discretization is accurate, while there is an error over the scale of single mesh points.

Why neural functionals suit statistical mechanics

We describe recent progress in the statistical mechanical description of many-body systems via machine learning combined with concepts from density functional theory and many-body simulations. We argue that the neural functional theory by Sammülleret al(2023Proc. Natl Acad. Sci.120e2312484120) gives a functional representation of direct correlations and of thermodynamics that allows for thorough quality control and consistency checking of the involved methods of artificial intelligence. Addressing a prototypical system we here present a pedagogical application to hard core particle in one spatial dimension, where Percus' exact solution for the free energy functional provides an unambiguous reference. A corresponding standalone numerical tutorial that demonstrates the neural functional concepts together with the underlying fundamentals of Monte Carlo simulations, classical density functional theory, machine learning, and differential programming is available online athttps://github.com/sfalmo/NeuralDFT-Tutorial.

Melting Is Well-Known, but Is It Also Well-Understood?

Contrary to continuous phase transitions, where renormalization group theory provides a general framework, for discontinuous phase transitions such a framework seems to be absent. Although the thermodynamics of the latter type of transitions is well-known and requires input from two phases, for melting a variety of one-phase theories and models based on solids has been proposed, as a generally accepted theory for liquids is (yet) missing. Each theory or model deals with a specific mechanism using typically one of the various defects (vacancies, interstitials, dislocations, interstitialcies) present in solids. Furthermore, recognizing that surfaces are often present, one distinguishes between mechanical or bulk melting and thermodynamic or surface-mediated melting. After providing the necessary preliminaries, we discuss both types of melting in relation to the various defects. Thereafter we deal with the effect of pressure on the melting process, followed by a discussion along the line of type of materials. Subsequently, some other aspects and approaches are dealt with. An attempt to put melting in perspective concludes this review.

Perspective: How to overcome dynamical density functional theory

We argue in favour of developing a comprehensive dynamical theory for rationalizing, predicting, designing, and machine learning nonequilibrium phenomena that occur in soft matter. To give guidance for navigating the theoretical and practical challenges that lie ahead, we discuss and exemplify the limitations of dynamical density functional theory (DDFT). Instead of the implied adiabatic sequence of equilibrium states that this approach provides as a makeshift for the true time evolution, we posit that the pending theoretical tasks lie in developing a systematic understanding of the dynamical functional relationships that govern the genuine nonequilibrium physics. While static density functional theory gives a comprehensive account of the equilibrium properties of many-body systems, we argue that power functional theory is the only present contender to shed similar insights into nonequilibrium dynamics, including the recognition and implementation of exact sum rules that result from the Noether theorem. As a demonstration of the power functional point of view, we consider an idealized steady sedimentation flow of the three-dimensional Lennard-Jones fluid and machine-learn the kinematic map from the mean motion to the internal force field. The trained model is capable of both predicting and designing the steady state dynamics universally for various target density modulations. This demonstrates the significant potential of using such techniques in nonequilibrium many-body physics and overcomes both the conceptual constraints of DDFT as well as the limited availability of its analytical functional approximations.

Comparative study of force-based classical density functional theory

We reexamine results obtained with the recently proposed density functional theory framework based on forces (force-DFT) [S. M. Tschopp et al., Phys. Rev. E 106, 014115 (2022)2470-004510.1103/PhysRevE.106.014115]. We compare inhomogeneous density profiles for hard sphere fluids to results from both standard density functional theory and from computer simulations. Test situations include the equilibrium hard sphere fluid adsorbed against a planar hard wall and the dynamical relaxation of hard spheres in a switched harmonic potential. The comparison to grand canonical Monte Carlo simulation profiles shows that equilibrium force-DFT alone does not improve upon results obtained with the standard Rosenfeld functional. Similar behavior holds for the relaxation dynamics, where we use our event-driven Brownian dynamics data as benchmark. Based on an appropriate linear combination of standard and force-DFT results, we investigate a simple hybrid scheme which rectifies these deficiencies in both the equilibrium and the dynamical case. We explicitly demonstrate that although the hybrid method is based on the original Rosenfeld fundamental measure functional, its performance is comparable to that of the more advanced White Bear theory.