Polymer Composites Informatics for Flammability, Thermal, Mechanical and Electrical Property Predictions
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908621551435776 |
|---|---|
| author | Tran, Huan Kim, Chiho Gurnani, Rishi Hvidsten, Oliver DeSimpliciis, Justin Ramprasad, Rampi Gadelrab, Karim Tuffile, Charles Molinari, Nicola Kitchaev, Daniil Kornbluth, Mordechai |
| author_facet | Tran, Huan Kim, Chiho Gurnani, Rishi Hvidsten, Oliver DeSimpliciis, Justin Ramprasad, Rampi Gadelrab, Karim Tuffile, Charles Molinari, Nicola Kitchaev, Daniil Kornbluth, Mordechai |
| contents | Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups. Traditional physics-based optimization methods for these parameters can be slow, labor-intensive, and costly, as they require physical manufacturing and testing. Here, we introduce a first step in extending Polymer Informatics, an AI-based approach proven effective for neat polymer design, into the realm of polymer composites. We curate a comprehensive database of commercially available polymer composites, develop a scheme for machine-readable data representation, and train machine-learning models for 15 flame-resistant, mechanical, thermal, and electrical properties, validating them on entirely unseen data. Future advancements are planned to drive the AI-assisted design of functional and sustainable polymer composites. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08407 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Polymer Composites Informatics for Flammability, Thermal, Mechanical and Electrical Property Predictions Tran, Huan Kim, Chiho Gurnani, Rishi Hvidsten, Oliver DeSimpliciis, Justin Ramprasad, Rampi Gadelrab, Karim Tuffile, Charles Molinari, Nicola Kitchaev, Daniil Kornbluth, Mordechai Soft Condensed Matter Materials Science Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups. Traditional physics-based optimization methods for these parameters can be slow, labor-intensive, and costly, as they require physical manufacturing and testing. Here, we introduce a first step in extending Polymer Informatics, an AI-based approach proven effective for neat polymer design, into the realm of polymer composites. We curate a comprehensive database of commercially available polymer composites, develop a scheme for machine-readable data representation, and train machine-learning models for 15 flame-resistant, mechanical, thermal, and electrical properties, validating them on entirely unseen data. Future advancements are planned to drive the AI-assisted design of functional and sustainable polymer composites. |
| title | Polymer Composites Informatics for Flammability, Thermal, Mechanical and Electrical Property Predictions |
| topic | Soft Condensed Matter Materials Science |
| url | https://arxiv.org/abs/2412.08407 |