RMD: Relaxation-Inspired Masked Diffusion Language Model for Crystal Generation

Language models offer a promising approach to crystal generation through unified sequence representations and flexible conditional modeling. However, standard autoregressive generation follows a unidirectional, left-to-right order, restricting predictions to the generated prefix and typically preventing earlier outputs from being revised using later structural information. To address these limitations, we present the first application of a discrete masked diffusion language model to crystal structural text generation, enabling bidirectional prediction and iterative recovery. To further address the challenge of identifying token positions that require continued adjustment, we propose Relaxation-inspired Masked Diffusion (RMD). Inspired by the assess–update–reassess organization of physical relaxation, RMD combines relaxation-score-driven position selection with selective state updates, allowing existing content to be revised as the visible context evolves. State-of-the-art results on the evaluated de novo generation and composition-conditioned crystal structure prediction benchmarks, support RMD's potential to produce high-quality candidates for stable crystal discovery. Our code and datasets are available at https://anonymous.4open.science/r/RMD.