# hyperai/awesome-ai4s

AI for Science 论文解读合集（持续更新ing），论文/数据集/教程下载：hyper.ai

Repository: https://github.com/hyperai/awesome-ai4s
Canonical: https://ross.abutalabs.com/products/awesome-ai4s
Homepage: https://hyper.ai
License: Apache-2.0
License Family: permissive
Last push: 2026-07-22T09:18:26+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 93, release rhythm 35, longevity 62
- inputs: {"age_days": 878, "days_push": 42, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3339, forks 518 (observed 2026-08-28T04:07:56.840563+00:00)

## What it is
A curated, continuously updated collection of annotated AI for Science research papers maintained by HyperAI, covering domains like biopharmaceuticals, materials chemistry, astrophysics, and weather forecasting. It serves as a reading list linking to paper interpretations, datasets, and tutorials hosted on hyper.ai.

## Use cases
- find AI papers applied to drug discovery
- learn how machine learning is used in science
- keep up with AI for Science research
- find datasets for scientific machine learning
- discover deep learning applications in chemistry and biology
- find tutorials on AI in scientific research

## When to choose
- you want a curated, annotated reading list of AI-for-Science papers
- you are exploring cross-domain applications of ML in science
- you want links to related datasets and tutorials

## When to avoid
- you need runnable software or code libraries
- you need exhaustive, systematic literature coverage rather than curated highlights
- you need peer-reviewed educational material

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, machine-learning, data-science
- domain: artificial-intelligence, machine-learning, bioinformatics, tutorials, awesome-lists
- platform: -
- tags: ai-for-science, paper-summaries, curated-list, biopharmaceuticals, materials-chemistry, weather-forecasting, research-papers, web-server

## Member repositories
- hyperai/awesome-ai4s (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:56.840563+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:41:05.501106+00:00, confidence not recorded.
  - readme: https://github.com/hyperai/awesome-ai4s (fetched 2026-08-28T04:07:56.840563+00:00, sha fd4169ce4fed)
  - homepage: https://hyper.ai (fetched 2026-08-29T09:34:44.199201+00:00, sha e37239f81954)
  - site_page: https://hyper.ai/en/docs (fetched 2026-08-29T09:34:44.208045+00:00, sha 8d5f1e15d322)
  - site_page: https://hyper.ai/en/about (fetched 2026-08-29T09:34:44.212116+00:00, sha ee4d10c19d9f)
  - site_page: https://hyper.ai/en/pricing (fetched 2026-08-29T09:34:44.210033+00:00, sha 2c1c9f0737b5)
- Data as of 2026-08-30T08:39:29.467469+00:00.