Our paper 'Unlearning to Rest: Machine unlearning as a method of mitigating design fixation in human-AI creative collaboration' was presented at the Design Research Society conference. We proposed machine unlearning as a novel approach to mitigating fixation in human-AI creative collaboration. Unlike fine-tuning methods that expand a model's knowledge base, unlearning removes specific concepts to create productive gaps in the model's representational space. We operationalise this with Unlearning to Rest, a prototype that applies weight-level concept suppression to Llama3.2:3b, suppressing a canonical attractor concept (“chair”) to create navigational impediments in the solution space.