arXiv:2512.22597v2 Announce Type: replace Abstract: Exploring molecular energy landscapes and identifying ground-state conformations are central challenges in computational chemistry. However, generating diverse low-energy conformers from molecular graphs remains expensive with traditional physics-based pipelines. Existing learning-based approaches remain fragmented: generative models capture conformational diversity but often lack reliable energy calibration, whereas deterministic predictors focus on a single structure and fail to represent ensemble variability. Here we introduce EnFlow, to o

Source: arXiv cs.LG — read the full report at the original publisher.

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