The study analyzes the results of a community effort and determines that machine learning greatly improves the estimation of the properties of diffusing particles.
Extending Brownian Motion and Connecting to Complex Systems
Since Albert Einstein provided a theoretical foundation for Robert Brown’s observation of the erratic or unpredictable movement of microscopic particles suspended within pollen grains, researchers have uncovered significant new findings that deviate quite a bit from the laws of Brownian motion in a variety of animate and inanimate systems, from biology to the stock market.
Anomalous diffusion, as scientists call it, extends the concept of Brownian motion and connects to disordered systems, non-equilibrium phenomena, flows of energy and information, and transport in living systems.
Machine Learning Revolutionizes Anomalous Diffusion Analysis: Real-World Tools Evaluated
Researchers have developed several methods for detecting the occurrence of anomalous diffusion using classical statistics. However, in the last years, the booming of machine learning has boosted the development of data-based methods to characterize anomalous diffusion from single trajectories, providing more refined tools for this problem.
Now, a group of scientists led by researchers from the University of Vic – Central University of Catalunya (Uvic-UCC) together with Optologic researcher Maciej Lewenstein in collaboration with colleagues from ICFO, the University of Gothenburg, the University of Potsdam, and the Universitat Politècnica de València, has provided the first assessment of conventional and novel methods for quantifying anomalous diffusion in a variety of realistic conditions through a community-based effort. The results of the assessment have been recently published in Nature Communications.
AnDi Challenge Spurs Research on Anomalous Diffusion
During the past year, the researchers launched an open competition to benchmark existing methods and to spur the invention of new approaches. The Anomalous Diffusion (AnDi) Challenge brought together a vibrating and multidisciplinary community of scientists working on this problem, involving more than 30 participants from 22 institutions and 11 countries. Ultimately, the analysis of the results obtained on a reference dataset provided an objective assessment of the performance of methods to characterize anomalous diffusion from single trajectories for three specific tasks: anomalous exponent inference, model classification, and trajectory segmentation.
In conclusion, this research not only definitively contributes to the definition of a diverse palette of tools and measures, but it also has the potential for these tools to become standard methods for the analysis of trajectories across a wide range of experiments, from the intricacies of atomic physics to the complexities of ecology. Furthermore, the outcome of this study reinforces the fundamental importance of community-based efforts in the pursuit of scientific advancement. Overall, this collaborative approach is instrumental in fostering progress and innovation within the scientific community.
Cited article: Muñoz-Gil et al. Objective comparison of methods to decode anomalous diffusion. Nature Communications on October 29, 2021. doi: 10.1038/s41467-021-26320-w
