From knots to crystals: Machine-learned potentials for self-assembling topological solitons in liquid crystals

Abstract

Knotted fields in classical and quantum systems have long been recognized for their nontrivial topologies and particlelike behavior, but practical applications have been limited by the difficulty of stabilizing them. Recently, stable knotted solitonic textures—heliknotons—were discovered in chiral liquid crystals, forming adaptive crystal assemblies via elastic distortion-mediated interactions. We use machine learning to develop single-site coarse-grained potentials that accurately capture these chiral anisotropic effective interactions. The resulting potentials accurately reproduce experimentally observed heliknoton assemblies and enable simulations at length scales and timescales far beyond the range of fine-grained continuum models. This general framework is readily transferable to other topological solitons, providing a powerful route to understand, predict, and ultimately control their collective behavior and dynamics.