arXiv:2605.26833v1 Announce Type: new Abstract: Polymers underpin applications across energy, healthcare, and materials science, yet their vast chemical space makes systematic discovery challenging. Most machine learning approaches represent polymers as molecular graphs of a single repeating unit, thereby missing both the periodicity of polymer chains and many-body interactions beyond pairwise bonds. We introduce Periodic-TDL, a deep learning framework built on periodic Vietoris-Rips complexes that capture many-body interactions across multiple spatial scales, followed by a hierarchical simpli
Emerging alongside advancements in deep learning, this research addresses the limitations of current machine learning approaches in polymer design, which often overlook critical periodic and many-body interactions.
This work introduces a foundational capability for accelerated and systematic discovery of advanced polymers, critical for numerous high-impact industries from energy and healthcare to materials science.
The ability to model polymers with greater structural nuance via periodic topological deep learning fundamentally alters the efficiency and scope of materials R&D, enabling the design of novel polymers with desired properties.
- · Materials Science R&D
- · Chemical Engineering
- · Pharmaceuticals
- · Energy Storage Industries
- · Traditional Trial-and-Error Materials Discovery
- · Companies reliant on conventional polymer development timelines
Accelerated development of new polymer materials with optimized properties for specific applications.
Reduced costs and time-to-market for products incorporating advanced polymeric components, driving innovation across sectors.
The creation of entirely new classes of materials previously inaccessible through conventional research methods, potentially leading to paradigm shifts in various industries.
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