Energy-Guided Generative Modeling for Low-Energy Molecular Structure Discovery

ArXi:2512.22597v2 Announce Type: replace 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