Alvaro Lanza

Quantifying the irreversibility and dissipation of nonequilibrium processes is crucial to understanding their behavior, assessing their possible capabilities, and characterizing their efficiency. We introduce a physical quantity that quantifies the irreversibility of stochastic Langevin systems from the observation of individual molecules’ displacements. Categorizing these displacements into a few groups based on their initial and final positions allows us to measure irreversibility precisely without the need to know the forces and magnitude of the fluctuations acting on the system. For short times, our model-free estimate of irreversibility is related to entropy production by a conditional fluctuation theorem. For short times and, in general, for stationary protocols, our estimate provides a lower bound to the average entropy production. We validate the method on single-molecule force spectroscopy experiments of proteins subject to force ramps. We show that irreversibility is sensitive to detailed features of the energy landscape underlying the protein folding dynamics and suggest how our methods can be employed to unveil key properties of protein folding processes.

Diffusive dynamics abound in nature and have been especially studied in physical, biological, and financial systems. These dynamics are characterised by a linear growth of the mean squared displacement (MSD) with time. Often, the conditions that give rise to simple diffusion are violated, and many systems, such as biomolecules inside cells, microswimmers, or particles in turbulent flows, undergo anomalous diffusion, featuring an MSD that grows following a power law with an exponent α. Precisely determining this exponent and the generalised diffusion coefficient provides valuable information on the systems under consideration, but it is a very challenging task when only a few short trajectories are available, which is common in non-equilibrium and living systems. Estimating the exponent becomes overwhelmingly difficult when the diffusive dynamics switches between different behaviours, characterised by different exponents α or diffusion coefficients K. We develop a method based on recurrent neural networks that successfully estimates the anomalous diffusion exponents and generalised diffusion coefficients of individual trajectories that switch between multiple diffusive states. Our method returns the α and K as a function of time and identifies the times at which the dynamics switches between different behaviours. We showcase the method’s capabilities on the dataset of the 2024 AnDi challenge.