Polymer Informatics Beyond Homopolymers
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913569849737216 |
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| author | Shukla, Shivank S. Kuenneth, Christopher Ramprasad, Rampi |
| author_facet | Shukla, Shivank S. Kuenneth, Christopher Ramprasad, Rampi |
| contents | Polymers are diverse and versatile materials that have met a wide range of material application demands. They come in several flavors and architectures, e.g., homopolymers, copolymers, polymer blends, and polymers with additives. Searching this enormous space for suitable materials with a specific set of property/performance targets is thus non-trivial, painstaking, and expensive. Such a search process can be made effective by the creation of rapid and accurate property predictors. In this work, we present a machine-learning framework to predict the thermal properties of homopolymers, copolymers, and polymer blends. A universal fingerprinting scheme capable of handling this entire polymer chemical class has been developed and a multi-task deep learning algorithm is trained simultaneously on a large dataset of glass transition, melting, and degradation temperatures. The developed models are accurate, fast, flexible, and scalable to other properties when suitable data become available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_12938 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Polymer Informatics Beyond Homopolymers Shukla, Shivank S. Kuenneth, Christopher Ramprasad, Rampi Soft Condensed Matter Computational Physics Polymers are diverse and versatile materials that have met a wide range of material application demands. They come in several flavors and architectures, e.g., homopolymers, copolymers, polymer blends, and polymers with additives. Searching this enormous space for suitable materials with a specific set of property/performance targets is thus non-trivial, painstaking, and expensive. Such a search process can be made effective by the creation of rapid and accurate property predictors. In this work, we present a machine-learning framework to predict the thermal properties of homopolymers, copolymers, and polymer blends. A universal fingerprinting scheme capable of handling this entire polymer chemical class has been developed and a multi-task deep learning algorithm is trained simultaneously on a large dataset of glass transition, melting, and degradation temperatures. The developed models are accurate, fast, flexible, and scalable to other properties when suitable data become available. |
| title | Polymer Informatics Beyond Homopolymers |
| topic | Soft Condensed Matter Computational Physics |
| url | https://arxiv.org/abs/2303.12938 |