DiffGA: Steering Frozen Diffusion Models for Materials Design via Evolutionary Search
Steering candidate generation toward user-defined objectives is central to inverse materials design, yet remains difficult when objectives are black-box, changing, or non-differentiable, as in many simulation- and experiment-derived properties. Generative models offer a solution but typically require retraining or fine-tuning for each new objective, demanding labeled data and substantial compute, or rely on differentiable guidance that is brittle to gradient noise and undefined for many practical targets. We present DiffGA, a retraining-free framework that injects evolutionary search into the sampling trajectory of a retrained diffusion model, evolving partially denoised states under an a...
| Authors | Shivang Agarwal, Rodrigo Wang, Kevin Ryczko et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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