arXiv:2510.16023v2 Announce Type: replace Abstract: Linear polymers, macromolecules formed from monomers covalently bonded into continuous chains, underpin countless technologies and are indispensable to modern life. While deep learning is advancing polymer science, existing methods typically represent the whole linear polymer solely through monomer-level descriptors, overlooking the global structural information inherent in polymer conformations, which ultimately limits their practical performance. Moreover, this important field still lacks a dedicated foundation model that can effectively su
The rapid advancements in deep learning models, particularly foundation models, are increasingly being applied to complex scientific domains like materials science, which are currently bottlenecks for industrial innovation.
A foundation model for polymer design can significantly accelerate the discovery and optimization of new materials with tailored properties, impacting various industries from aerospace to medicine.
The ability to generate and predict polymer structures based on global conformational information shifts material design from empirical trial-and-error to a more data-driven, generative approach.
- · Material science R&D
- · Chemical manufacturing
- · Pharmaceuticals
- · AI model developers
- · Traditional empirical material design methods
- · Labs with limited computational capabilities
Faster development cycle for advanced polymer materials.
New polymer-based products and applications become commercially viable sooner, impacting various sectors.
Enhanced material capabilities could lead to breakthroughs in other fields that rely on advanced materials, such as energy storage or biotechnology.
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Read at arXiv cs.LG