This repository is a curated collection of AI for Science (AI4S) research papers, focusing on applications across various scientific disciplines like biopharmaceuticals, healthcare, materials chemistry, zoology, botany, meteorology, astronomy, and natural disasters. It aims to provide researchers and practitioners with an accessible overview of cutting-edge AI applications in scientific discovery, addressing the need for greater research reproducibility and lower technical barriers in the rapidly evolving AI4S landscape.

How It Works

The project compiles and categorizes summaries of recent AI for Science research papers, often linking to detailed Chinese interpretations and original publications. It covers a broad spectrum of AI techniques, including deep learning, graph neural networks, transformers, and large language models, applied to diverse scientific problems. The categorization by discipline allows users to quickly navigate and find relevant advancements in their specific fields.

Quick Start & Requirements

This repository is a curated list of papers and does not have a direct installation or execution command. Access to the linked papers and their associated code (if available) would be required for practical use.

Highlighted Details

• Extensive coverage of AI applications in biopharmaceuticals, with over 50 papers detailed.
• Significant focus on AI in healthcare, including diagnostics, prognostics, and medical imaging.
• Broad exploration of AI in materials chemistry, highlighting discoveries of new materials and property predictions.
• Includes papers on AI for zoology, botany, agriculture, forestry, animal husbandry, meteorology, astronomy, and natural disaster analysis.
• Features policy discussions related to AI in science and government initiatives.

Maintenance & Community

The project is maintained by "HyperAI超神经" and is continuously updated. It encourages community contributions of excellent research findings and provides contact information for reporting needs.

Licensing & Compatibility

The licensing of the underlying research papers varies by publication. The repository itself appears to be shared for informational purposes, with links to original sources.

Limitations & Caveats

The repository primarily provides summaries and links to external resources. The depth of technical detail for each AI application may vary, and direct access to the original research papers and their implementations is necessary for full evaluation.