Dual-Axis RCCL: Representation-Complete Convergent Learning for Organic Chemical Space

• URL: http://arxiv.org/abs/2512.14418v2
• Date: Wed, 17 Dec 2025 05:18:23 GMT
• Title: Dual-Axis RCCL: Representation-Complete Convergent Learning for Organic Chemical Space
• Authors: Dejun Hu, Zhiming Li, Jia-Rui Shen, Jia-Ning Tu, Zi-Hao Ye, Junliang Zhang,
• Abstract summary: We introduce a Dual-Axis Representation-Complete Convergent Learning (RCCL) strategy, enabled by a molecular representation.<n>This framework formalizes representation completeness, establishing a basis for constructing principled datasets that support convergent learning.<n>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.
• Score: 5.782261680001994
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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