AI RESEARCH
DGLD: Domain-Gated Latent Diffusion for the Discovery of Novel Energetic Materials
arXiv CS.AI
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ArXi:2605.26540v1 Announce Type: cross Energetic-materials performance gains translate directly into reduced propellant mass, smaller warheads, and efficient civilian gas-generators, yet no new HMX-class compound has been disclosed in fifteen years. Designing one is a sparse-label problem: of ~66 k labelled CHNO molecules only ~3 k carry experimental or DFT-quality measurements, and naive generative models trained on the full mixture either memorise the high-performance tail or extrapolate without calibration. We.