Multi-property Feynman-Kac Steering of Crystal Diffusion Models

Pretrained diffusion models provide powerful priors for generating new materials, including inorganic crystals, but generating useful materials requires satisfying specific chemical and physical properties, from conductivity to band gaps. Existing approaches either retrain models for new target properties or steer only the atom coordinates and lattice during generation, leaving composition unchanged despite its fundamental role in determining physical properties. We present FK-ReMask, an inference-time, gradient-free method that steers both the continuous and discrete channels of a pretrained crystal diffusion model. During generation, FK-ReMask maintains multiple denoising trajectories and replicates promising candidates using Feynman–Kac particle selection. However, selection alone is insufficient: in the atom-type channel, committed elements cannot be revised, so cloning propagates existing compositions and limits the population's ability to explore new ones. We address this with an atom re-masking mechanism, which reopens committed sites during the generation process and allows alternative compositions to emerge. Across four electronic, photovoltaic, and mechanical objectives, our method improves the yield of SUN (stable, unique, novel) and property-on-target materials. We further demonstrate joint steering of three photovoltaic properties, showing that multiple physical objectives can be specified entirely at inference time without retraining.