Key Findings
DiffGA outperforms diffusion-only, purely evolutionary, and fine-tuned baselines in the rate of stable, unique, and novel hits across three crystal-design tasks using a frozen MatterGen backbone. The framework allows for the steering of generative models toward user-defined objectives that are non-differentiable or black-box, which are typical of simulation- and experiment-derived properties. Furthermore, the integration of a surrogate-accelerated pipeline preserves the fidelity of the discovery process at a fraction of the computational cost.
Technical Details
The framework injects evolutionary search directly into the sampling trajectory of a pretrained diffusion model, evolving partially denoised horizon states under an arbitrary black-box oracle. To mitigate the high cost of oracle evaluations, DiffGA employs an on-the-fly Bayesian surrogate. This surrogate utilizes a Gaussian process fit on graph embeddings from state-of-the-art machine-learned force fields (MLFFs). Under an uncertainty-aware acquisition rule, the surrogate replaces expensive property-evaluation steps once it demonstrates the ability to rank unseen generations. The fidelity of the MLFF oracle was quantified using density functional theory to determine the regimes in which the proxy-driven discovery is reliable.
Background & Context
Inverse materials design typically requires steering generative models toward specific goals. Conventional methods rely on expensive fine-tuning with large labeled datasets or differentiable score guidance, which is often brittle to gradient noise and inapplicable to non-differentiable targets. While Genetic Algorithms (GAs) natively support black-box scoring, they often produce structurally implausible configurations. DiffGA addresses these limitations by combining the high-fidelity sampling of diffusion models with the direct search capabilities of evolutionary algorithms.
Significance & Outlook
By removing the need for retraining or architectural modifications for every new objective, DiffGA provides a flexible pipeline for materials discovery. The use of a frozen backbone ensures that the model does not require substantial compute for every new target. Testing on representative samples confirms that top hits survive first-principles re-evaluation as physically realistic materials.
Source: https://arxiv.org/html/2610.14999v1
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