PolyTAO
AI Models Apache-2.0 (Free)
About
A Transformer-Assisted Oriented pretrained model for on-demand polymer generation (conditional generative LLM). Generates polymers with 15 predefined fundamental properties. Achieves ~99.3% chemical validity in top-1 generation.
Citation
Qiu, H.; Sun, Z.-Y. On-Demand Reverse Design of Polymers with PolyTAO. npj Computational Materials 10, 273 (2024). DOI:10.1038/s41524-024-01466-5
Frequently Asked Questions
What is PolyTAO?
PolyTAO is a ai models application available in the Paramus App Store. A Transformer-Assisted Oriented pretrained model for on-demand polymer generation (conditional generative LLM). Generates polymers with 15 predefined fundamental properties. Achieves ~99.3% chemical validity in top-1 generation.
Is PolyTAO free to use?
Yes. PolyTAO is distributed under the Apache-2.0 (Free) license and is available at no cost through the Paramus App Store.
How do I install PolyTAO?
PolyTAO is installed through Paramus Chemistry OS, an on-premise Windows platform for computational chemistry. Open the Paramus App Store in your local installation and select PolyTAO for one-click deployment.
What type of application is PolyTAO?
PolyTAO belongs to the “AI Models” category in the Paramus App Store. It runs on Paramus Chemistry OS and can also be accessed through Paramus Cloud for supported workflows.
What platform does PolyTAO run on?
PolyTAO runs on Paramus Chemistry OS, a Windows-based on-premise platform that provides local compute power for demanding simulations. It requires a Paramus OS installation with appropriate hardware resources.
Can PolyTAO be automated or integrated with AI workflows?
Yes. PolyTAO is available as part of the Paramus ecosystem which supports MCP (Model Context Protocol) tools for AI-driven automation. This enables integration with large language models and automated research pipelines.
How should I cite PolyTAO in publications?
The recommended citation for PolyTAO is: Qiu, H.; Sun, Z.-Y. On-Demand Reverse Design of Polymers with PolyTAO. npj Computational Materials 10, 273 (2024). DOI:10.1038/s41524-024-01466-5