GO-Diff: Data-free and amortized global structure optimization

Nikolaj Rønne; Tejs Vegge; Arghya Bhowmik

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arXiv · 2510.13448

GO-Diff: Data-free and amortized global structure optimization

Nikolaj Rønne·Tejs Vegge·Arghya Bhowmik

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Abstract

We introduce GO-Diff, a diffusion-based method for global structure optimization that learns to directly sample low-energy atomic configurations without requiring prior data or explicit relaxation. GO-Diff is trained from scratch using a Boltzmann-weighted score-matching loss, leveraging only the known energy function to guide generation toward thermodynamically favorable regions. The method operates in a two-stage loop of self-sampling and model refinement, progressively improving its ability to target low-energy structures. Compared to traditional optimization pipelines, GO-Diff achieves competitive results with significantly fewer energy evaluations. Moreover, by reusing pretrained models across related systems, GO-Diff supports amortized optimization - enabling faster convergence on new tasks without retraining from scratch.

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physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci cs.CE

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Nikolaj Rønne, Tejs Vegge, Arghya Bhowmik. 2025-10-15. GO-Diff: Data-free and amortized global structure optimization. https://arxiv.org/abs/2510.13448

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