Dual-Axis RCCL: Representation-Complete Convergent Learning for Organic Chemical Space
This addresses the problem of model generalization in molecular and materials modeling for researchers, providing a foundation for interpretable and data-efficient molecular intelligence.
The paper tackles the challenge of achieving convergent learning across vast chemical space by introducing a Dual-Axis RCCL strategy with a novel molecular representation, resulting in graph neural networks trained on their FD25 dataset achieving approximately 1.0 kcal/mol MAE prediction error across external benchmarks.
Machine learning is profoundly reshaping molecular and materials modeling; however, given the vast scale of chemical space (10^30-10^60), it remains an open scientific question whether models can achieve convergent learning across this space. We introduce a Dual-Axis Representation-Complete Convergent Learning (RCCL) strategy, enabled by a molecular representation that integrates graph convolutional network (GCN) encoding of local valence environments, grounded in modern valence bond theory, together with no-bridge graph (NBG) encoding of ring/cage topologies, providing a quantitative measure of chemical-space coverage. This framework formalizes representation completeness, establishing a principled basis for constructing datasets that support convergent learning for large models. Guided by this RCCL framework, we develop the FD25 dataset, systematically covering 13,302 local valence units and 165,726 ring/cage topologies, achieving near-complete combinatorial coverage of organic molecules with H/C/N/O/F elements. Graph neural networks trained on FD25 exhibit representation-complete convergent learning and strong out-of-distribution generalization, with an overall prediction error of approximately 1.0 kcal/mol MAE across external benchmarks. Our results establish a quantitative link between molecular representation, structural completeness, and model generalization, providing a foundation for interpretable, transferable, and data-efficient molecular intelligence.