LLMs stop guessing bandgaps: Motif turns them into chemistry taxonomies, cutting 2D perovskite OOD error from 0.122 to 0.111 eV
On 2D halide perovskite bandgap prediction, Motif moves the large language model from emitting numbers to generating chemical taxonomies offline, which then define environments for invariant feature selection. Across six leave-one-spacer-family-out folds, mean OOD MAE falls from the kNN+GBT baseline's 0.122 eV to 0.111 eV; under a frozen dev/test protocol the deployable configuration lowers electronic bandgap error from 0.123 to 0.117 eV and optical bandgap error from 0.181 to 0.173 eV.