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

Authors

Chengchun Liu, Wendi Cai, Boxuan Zhao, Fanyang Mo

Published in

Journal of chemical theory and computation. Jul 12, 2026. Epub Jul 12, 2026.

Abstract

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.

PMID:
42437350
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.

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