Dynamic Catalytic Literature Extraction This project aims to develop a robust system for extracting relevant information from scientific literature, with a particular emphasis on catalytic processes and dynamic phenomena. The extracted data can be used for various applications, such as knowledge discovery, research trend analysis, and catalyst design optimization.
Features Automated literature parsing: The system can automatically parse and extract structured information from scientific publications in various formats (e.g., PDF, XML, HTML). Named entity recognition (NER): Identify and classify key entities, such as chemical compounds, materials, reactions, and experimental conditions. Relation extraction: Extract semantic relationships between identified entities, enabling the construction of knowledge graphs. Dynamic process modeling: Capture and model dynamic aspects of catalytic processes, including reaction kinetics, catalyst deactivation, and process optimization. Interactive user interface: Provide a user-friendly interface for querying and visualizing the extracted information.
Clone the repository:
git clone https://github.com/yourusername/dynamic-catalytic-literature-extraction.git
Install the required dependencies:
pip install -r requirements.txt
Detailed usage instructions and examples will be provided in the docs directory.
We welcome contributions from the community! Please refer to the CONTRIBUTING.md file for guidelines on how to contribute to this project.
This project is licensed under the MIT License.
This project was inspired by the growing need for efficient information extraction and knowledge discovery in the field of catalysis. We would like to acknowledge the contributions of the open-source community and the researchers whose work has been invaluable in developing this system.
For any questions or inquiries, please contact the project maintainers at maintainers@example.com.
Feel free to modify or expand upon this draft as needed to better suit your specific project requirements.