Study of self-propelling particles at the molecular scale

Active matter systems have the ability to exhibit self-propulsion by consuming energy to produce mechanical work, staying out of equilibrium. They occur on a vast range of scales, from herding mammals and flocking birds down to bacteria. A major goal in the field is the development of artificial micro- and nano-scale particles capable of self-propulsion, so-called swimmers. These swimmers hold the potential for groundbreaking applications, such as targeted drug delivery, non-invasive microsurgery, and water purification. However, achieving propulsion at such small scales is severely limited, prompting the search for novel propulsion mechanisms.

This thesis demonstrates a propulsion mechanism for a small molecule, nitromethane, via all-atom molecular dynamics simulations. The molecule is subject to a high energy vibrational excitation which is then released anisotropically onto the surrounding water solvent. This results in propulsion velocity bursts that are able to enhance the translational diffusion of the molecule. The nitromethane model thus constitutes the smallest example of a self-propelling particle.

Finally, a class of state-of-the-art machine learning interatomic potentials, based on neural networks, is presented and utilized for the study of thermophoresis, a phenomenon closely related to the propulsion mechanism of nitromethane and with a marked sensitivity to solute-solvent interactions. Neural network potentials allow the exploration of large scale systems with precision rivaling first-principles quantum calculations. These new algorithms are being developed at a fast pace and allow for the accurate characterization of condensed matter systems, a critical ingredient in the development of functionalized self-propelling particles.