Juliacon 2024

We first present the hydrodynamic equations for lyotropic (concentration-dependent) Liquid Crystals (LCs), derived via the thermodynamic GENERIC framework. Next, we showcase the development of a .jl package to solve these equations, combining 1) finite differences (inspired by DiffEqOperators.jl) and 2) the Lattice Boltzmann method (found in Trixi.jl). Solving in 2D and 3D at equilibrium and under different flows, we demonstrate that our methodology allows the prediction of experimental LC data.

We introduce Cairn.jl, a Julia library of state-of-the-art active learning algorithms for training machine learning interatomic potentials for molecular dynamics simulation. This package provides a unifying Julia-based platform for rapid comparison and prototyping of active learning schemes, including a novel technique based on kernel Stein discrepancy for data querying and labeling.