Difference between revisions of "Science Agents"

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* 2025-01: [https://arxiv.org/abs/2501.13299 Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents]
 
* 2025-01: [https://arxiv.org/abs/2501.13299 Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents]
 
* 2025-02: [https://arxiv.org/abs/2502.13025 Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks]
 
* 2025-02: [https://arxiv.org/abs/2502.13025 Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks]
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* 2025-06: [https://arxiv.org/abs/2506.00794 Predicting Empirical AI Research Outcomes with Language Models]
  
 
==Adapting LLMs to Science==
 
==Adapting LLMs to Science==
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* 2023-12: [https://doi.org/10.1038/s41586-023-06792-0 Autonomous chemical research with large language models] (Coscientist)
 
* 2023-12: [https://doi.org/10.1038/s41586-023-06792-0 Autonomous chemical research with large language models] (Coscientist)
 
* 2024-11: [https://www.nature.com/articles/s41467-024-54457-x An automatic end-to-end chemical synthesis development platform powered by large language models]
 
* 2024-11: [https://www.nature.com/articles/s41467-024-54457-x An automatic end-to-end chemical synthesis development platform powered by large language models]
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* 2025-06: [https://paper.ether0.ai/ Training a Scientific Reasoning Model for Chemistry]
  
 
==LLMs Optimized for Science==
 
==LLMs Optimized for Science==

Latest revision as of 12:23, 5 June 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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