Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space

MAX phases (M _n+1AX _n ), precursors to MXenes, span a vast compositional space, motivating efficient computational screening for synthesisable candidates. We employ CrystaLLM-π, a large language model fine-tuned on 6179 double transition-metal MAX phases, and demonstrate its ability to generate novel structures consistent with known experimental trends. Using a conditioning vector with two dimensions (a statistically derived MXene derivative count and a surrogate for A-site binding energy), the model was able to target MXene-favourable regions of phase space for generation. Specific condition vectors double novel stable structure generation rates versus unconditioned baselines. Of ten compositionally novel candidates, five exhibit DFT-validated (meta)stability (E _hull < 0.050 eV per atom). This work showcases the potential for autoregressive generative models to explore targeted materials' spaces, offering a scalable framework for accelerated discovery in compositionally complex systems.