Difference between revisions of "Science Agents"

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* 2025-08: [https://arxiv.org/abs/2508.15763 Intern-S1: A Scientific Multimodal Foundation Model]
 
* 2025-08: [https://arxiv.org/abs/2508.15763 Intern-S1: A Scientific Multimodal Foundation Model]
 
* 2025-11: [https://pubs.aip.org/aip/jcp/article/163/18/184110/3372267/A-foundation-model-for-atomistic-materials A foundation model for atomistic materials chemistry]
 
* 2025-11: [https://pubs.aip.org/aip/jcp/article/163/18/184110/3372267/A-foundation-model-for-atomistic-materials A foundation model for atomistic materials chemistry]
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* 2025-11: [https://arxiv.org/abs/2511.15684 Walrus: A Cross-Domain Foundation Model for Continuum Dynamics]
  
 
===Regression (Data Fitting)===
 
===Regression (Data Fitting)===
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=Genuine Discoveries=
 
=Genuine Discoveries=
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* 2025-11: [https://cdn.openai.com/pdf/4a25f921-e4e0-479a-9b38-5367b47e8fd0/early-science-acceleration-experiments-with-gpt-5.pdf Early science acceleration experiments with GPT-5]
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* '''Math:'''
 
* '''Math:'''
 
** 2023-07: [https://www.nature.com/articles/s41586-023-06004-9?utm_source=chatgpt.com Faster sorting algorithms discovered using deep reinforcement learning]
 
** 2023-07: [https://www.nature.com/articles/s41586-023-06004-9?utm_source=chatgpt.com Faster sorting algorithms discovered using deep reinforcement learning]
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** 2023-07: [https://www.nature.com/articles/s41586-023-06415-8 De novo design of protein structure and function with RFdiffusion]
 
** 2023-07: [https://www.nature.com/articles/s41586-023-06415-8 De novo design of protein structure and function with RFdiffusion]
 
** 2025-11: [https://www.nature.com/articles/s41586-025-09721-5 Atomically accurate de novo design of antibodies with RFdiffusion]
 
** 2025-11: [https://www.nature.com/articles/s41586-025-09721-5 Atomically accurate de novo design of antibodies with RFdiffusion]
 +
** 2025-11: [https://x.com/GoogleDeepMind/status/1993350293703016451?s=20 ]
 
* '''Material Discovery:'''
 
* '''Material Discovery:'''
−
** 2023-11: [https://doi.org/10.1038/s41586-023-06735-9 Scaling deep learning for materials discovery]
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** 2023-11: [https://deepmind.google/blog/alphafold-five-years-of-impact/ AlphaFold: Five years of impact]
  
 
=See Also=
 
=See Also=
 
* [[AI agents]]
 
* [[AI agents]]
 
* [https://nanobot.chat/ Nanobot.chat]: Intelligent AI for the labnetwork @ mtl.mit.edu forum
 
* [https://nanobot.chat/ Nanobot.chat]: Intelligent AI for the labnetwork @ mtl.mit.edu forum

Latest revision as of 10:03, 26 November 2025

AI Use-cases for Science

Literature

LLM extract data from papers

AI finding links in literature

(Pre) Generate Articles

Explanation

Autonomous Ideation

Adapting LLMs to Science

AI/LLM Control of Scientific Instruments/Facilities

AI/ML Methods tailored to Science

Science Foundation Models

Regression (Data Fitting)

Tabular Classification/Regression

Symbolic Regression

Literature Discovery

Commercial

Bio

AI/ML Methods in Science

Imaging

Materials

Chemistry

Biology

Medicine

See: AI_Agents#Medicine

Successes

AI/ML Methods co-opted for Science

Mechanistic Interpretability

Train large model on science data. Then apply mechanistic interpretability (e.g. sparse autoencoders, SAE) to the feature/activation space.

Uncertainty

Science Benchmarks

Science Agents

Reviews

Specific

Science Multi-Agent Setups

AI Science Systems

Inorganic Materials Discovery

Materials Characterization

Chemistry

Bio

LLMs Optimized for Science

Impact of AI in Science

Related Tools

Literature Search

Data Visualization

Generative

Chemistry

Science Datasets

Genuine Discoveries

See Also

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