Difference between revisions of "Science Agents"

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===Imaging===
 
===Imaging===
 
* 2025-05: [https://arxiv.org/abs/2505.08176 Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations] (blog: [https://phzwart.github.io/behindthenoise/ Behind the Noise])
 
* 2025-05: [https://arxiv.org/abs/2505.08176 Behind the Noise: Conformal Quantile Regression Reveals Emergent Representations] (blog: [https://phzwart.github.io/behindthenoise/ Behind the Noise])
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===Materials===
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* 2025-03: [https://arxiv.org/abs/2503.03965 All-atom Diffusion Transformers: Unified generative modelling of molecules and materials]
  
 
===Chemistry===
 
===Chemistry===
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=Impact of AI in Science=
 
=Impact of AI in Science=
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* 2024-11: [https://aidantr.github.io/files/AI_innovation.pdf Artificial Intelligence, Scientific Discovery, and Product Innovation]
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* 2024-11: <strike>[https://aidantr.github.io/files/AI_innovation.pdf Artificial Intelligence, Scientific Discovery, and Product Innovation]</strike>
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** 2025-05: Retraction: [https://economics.mit.edu/news/assuring-accurate-research-record Assuring an accurate research record]
 
* 2025-02: [https://arxiv.org/abs/2502.05151 Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation]
 
* 2025-02: [https://arxiv.org/abs/2502.05151 Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation]
  

Latest revision as of 15:28, 22 May 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

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

Chemistry

LLMs Optimized for Science

Impact of AI in Science

Related Tools

Literature Search

Data Visualization

Generative

Chemistry

Science Datasets

See Also

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