MeanFlux: Few-Step Crystal Generation for Accelerated Materials Discovery

Current generative models for materials discovery produce high-quality candidates but suffer from massive sampling bottlenecks, often requiring dozens to thousands of network function evaluations (NFEs) per structure. We introduce MeanFlux, a diffusion transformer that accelerates generation by learning the average velocity across intervals of a generative trajectory. Operating in the per-atom latent space of a frozen autoencoder, MeanFlux generates viable crystals in just 1 to 16 steps. On the MP-20 benchmark, a 130M-parameter model matches the state-of-the-art yield of metastable, unique, and novel candidates while using one-sixth the inference compute. This efficiency extends to crystal structure prediction, where MeanFlux requires substantially less sampling compute than prior methods and scales predictably with model size. On LeMat-GenBench, MeanFlux produces an order of magnitude more viable crystals per compute unit than comparably pretrained models. With negative-aware fine-tuning (NFT) on a crystal reward, the MP-20 model reaches 22.5% relaxed LeMat-GenBench mSUN, twice the conditional base model, at the same sampling cost. These results demonstrate that few-step generation preserves structural quality while unlocking the extreme throughput required for large-scale materials screening.