A Cross-Domain Graph Learning Protocol for Single-Step Molecular Geometry Refinement

Accurate molecular geometries are indispensable for predictive quantum chemistry, yet iterative density functional theory (DFT) optimization remains a major computational bottleneck in large-scale screening. Here, we introduce GeoOpt-Net, a deterministic, SE(3)-equivariant single-step geometry refinement network that maps inexpensive force-field conformers directly to B3LYP/TZVP-quality structures. Trained using a two-stage multifidelity protocol with theory-aware feature modulation, GeoOpt-Net learns transferable geometric priors and calibrates them to target-level quantum accuracy in a single forward pass. Under strictly matched B3LYP/TZVP conditions, GeoOpt-Net achieves structural deviations on the order of 10-4 Å and single-point energy deviations on the order of 10-4 kcal mol-1, outperforming classical, semiempirical, and neural potential-based approaches. Notably, its predicted geometries satisfy all standard DFT convergence criteria for 65.0% (loose) and 33.4% (default) of molecules, whereas baseline methods remain near zero, substantially reducing subsequent optimization effort. By replacing iterative relaxation with deterministic single-step refinement, GeoOpt-Net offers a scalable and physically consistent protocol for accelerating high-throughput quantum-chemical workflows.