Uni-Macro-FRPN: Full-Resolution and Cross-Scale Learning for Polymers
- 1School of Emergent Soft Matter, South China University of Technology
- 2Guangdong Provincial Key Laboratory of Functional and Intelligent Hybrid Materials and Devices
- 3South China University of Technology
Abstract
Polymer properties are governed by interactions across scales. Existing polymer models commonly retain either monomer chemistry without the polymer graph, or the polymer graph with simplified monomer chemistry. Here, we present Uni-Macro-FRPN (FRPN), a Full-Resolution Polymer Network unifying atom-level and within-monomer structural encoding with an explicit monomer-instance graph of a polymer chain. Its two Transformers jointly learn atom-informed monomer semantics, sequence order, and chain topology. Using BigSMILES to construct monomer semantics and polymer graphs, FRPN achieves 86.4% accuracy and 90.6% ROC–AUC on Block Copolymer Database (BCDB) lamellar-versus-non-lamellar classification, outperforming the evaluated baselines and supporting joint reasoning over monomer semantics and chain structure. Ablation results support a contribution from joint representation beyond the tested increases in parameter count. On the linear homopolymer benchmark, monomer-centric learning remains competitive, identifying a boundary case in which polymer-scale organization is relatively simple. In addition, existing models are primarily developed around linear polymers. To test whether this full-chain representation generalizes beyond linear polymers, we construct an all-atom moleculardynamics (MD) benchmark of 1640 datapoints spanning diverse monomer chemistries, sequence orderings, chain topologies, and physical properties. FRPN achieves the strongest overall performance on this topology-rich benchmark, and the accompanying diagnostics suggest a benefit from jointly modeling monomer chemistry and polymer structure within one architecture. Taken together, FRPN provides a practical route for moving polymer representation learning beyond SMILESbased descriptions by using BigSMILES to connect monomer semantics with explicit polymer-chain topology, and establishes a foundation for full-chain, cross-scale polymer modeling. Its leading overall performance among the evaluated models on both the real-world BCDB benchmark and the topology-rich MD benchmark further suggests a promising direction for polymer informatics: future polymer prediction models should treat polymers not only as collections of monomer descriptors, but as complete multiscale chemical and topological objects.
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BibTeX
@misc{wu2026unimacrofrpn,
title = {{Uni-Macro-FRPN: Full-Resolution and Cross-Scale Learning for Polymers}},
author = {Jintao Wu and Yiran Shan and Rui Zhang},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009049/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009049/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Uni-Macro-FRPN: Full-Resolution and Cross-Scale Learning for Polymers
AU - Jintao Wu
AU - Yiran Shan
AU - Rui Zhang
PY - 2026
DA - 2026/09/18
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009049/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009049/v1
AB - Polymer properties are governed by interactions across scales. Existing polymer models commonly retain either monomer chemistry without the polymer graph, or the polymer graph with simplified monomer chemistry. Here, we present Uni-Macro-FRPN (FRPN), a Full-Resolution Polymer Network unifying atom-level and within-monomer structural encoding with an explicit monomer-instance graph of a polymer chain. Its two Transformers jointly learn atom-informed monomer semantics, sequence order, and chain topology. Using BigSMILES to construct monomer semantics and polymer graphs, FRPN achieves 86.4% accuracy and 90.6% ROC–AUC on Block Copolymer Database (BCDB) lamellar-versus-non-lamellar classification, outperforming the evaluated baselines and supporting joint reasoning over monomer semantics and chain structure. Ablation results support a contribution from joint representation beyond the tested increases in parameter count. On the linear homopolymer benchmark, monomer-centric learning remains competitive, identifying a boundary case in which polymer-scale organization is relatively simple. In addition, existing models are primarily developed around linear polymers. To test whether this full-chain representation generalizes beyond linear polymers, we construct an all-atom moleculardynamics (MD) benchmark of 1640 datapoints spanning diverse monomer chemistries, sequence orderings, chain topologies, and physical properties. FRPN achieves the strongest overall performance on this topology-rich benchmark, and the accompanying diagnostics suggest a benefit from jointly modeling monomer chemistry and polymer structure within one architecture. Taken together, FRPN provides a practical route for moving polymer representation learning beyond SMILESbased descriptions by using BigSMILES to connect monomer semantics with explicit polymer-chain topology, and establishes a foundation for full-chain, cross-scale polymer modeling. Its leading overall performance among the evaluated models on both the real-world BCDB benchmark and the topology-rich MD benchmark further suggests a promising direction for polymer informatics: future polymer prediction models should treat polymers not only as collections of monomer descriptors, but as complete multiscale chemical and topological objects.
ER -