Dead or alive: Distinguishing active from passive particles using supervised learning

A longstanding open question in the field of dense disordered matter is how precisely structure and the dynamics are related to each other. With the advent of machine learning, it has become possible to agnostically predict the dynamic propensity of a particle in a dense liquid based on its local structural environment. Thus far, however, these machine learning studies have focused almost exclusively on simple liquids composed of passive particles. Here we consider a mixture of both passive and active (i.e. self-propelled) Brownian particles, with the aim to identify the active particles from minimal local structural information. We find that the established machine learning approaches for passive systems are ineffective for our goal, implying that dynamic propensity and non-equilibrium activity carry a fundamentally different structural signature. To distinguish passive from active particles, we instead develop a pseudo-static machine learning method that uses both local structural order parameters and their averaged fluctuations as input. Our final neural network is able to detect with almost 100% accuracy which particles are active and which ones are not. Hence, our machine learning model can identify distinct dynamical single-particle properties with minimal dynamical information. Ultimately, these efforts might also find relevance in the context of biological active glasses such as confluent cell layers, where subtle changes in the microstructure can hint at pathological changes in cell dynamics