Mathematical Invariant-Enabled Topological Neural Networks for Molecular and Materials Property Prediction
Existing molecular and materials learning approaches often rely on a limited set of structural representations, which may capture only selected aspects of complex three-dimensional structure. Here, we introduce mathematical invariant-enabled topological neural networks (MITNNs), a framework that represents complex structures through multiple complementary mathematical views and integrates them with topological neural architectures. MITNNs combine multiscale invariants from topology, spectral theory, commutative algebra, differential geometry, and discrete curvature, capturing complementary structural information from the same system. Systematic invariant-subset, architecture-subset, and e...
| Authors | Yiming Ren, Xiang Liu, Mustafa Hajij et al. |
|---|---|
| Primary category | q-bio.BM |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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