Difference between revisions of "Science Agents"

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==AI/ML Methods tailored to Science==
 
==AI/ML Methods tailored to Science==
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===Science Foundation Models===
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* 2025-08: [https://arxiv.org/abs/2508.15763 Intern-S1: A Scientific Multimodal Foundation Model]
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===Regression (Data Fitting)===
 
===Regression (Data Fitting)===
 
* 2024-06: [https://arxiv.org/abs/2406.14546 Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data]: training on (x,y) pairs enables inferring underlying function (define it in code, invert it, compose it)
 
* 2024-06: [https://arxiv.org/abs/2406.14546 Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data]: training on (x,y) pairs enables inferring underlying function (define it in code, invert it, compose it)
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* [https://www.radical-ai.com/ Radical AI]: Material simulation/design
 
* [https://www.radical-ai.com/ Radical AI]: Material simulation/design
 
* [https://www.autoscience.ai/ Autoscience] ([https://www.autoscience.ai/blog/meet-carl-the-first-ai-system-to-produce-academically-peer-reviewed-research Carl])
 
* [https://www.autoscience.ai/ Autoscience] ([https://www.autoscience.ai/blog/meet-carl-the-first-ai-system-to-produce-academically-peer-reviewed-research Carl])
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* [https://periodic.com/ Periodic Labs]
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====Bio====
 
====Bio====
 
* [https://www.bioptimus.com/ Bioptimus]
 
* [https://www.bioptimus.com/ Bioptimus]
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* 2025-03: [https://arxiv.org/abs/2503.16351 Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences]
 
* 2025-03: [https://arxiv.org/abs/2503.16351 Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences]
 
* 2025-08: RosettaFold 3: [https://www.biorxiv.org/content/10.1101/2025.08.14.670328v2 Accelerating Biomolecular Modeling with AtomWorks and RF3]
 
* 2025-08: RosettaFold 3: [https://www.biorxiv.org/content/10.1101/2025.08.14.670328v2 Accelerating Biomolecular Modeling with AtomWorks and RF3]
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* 2025-09: [https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 Generative design of novel bacteriophages with genome language models]
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* 2025-10: [https://www.science.org/doi/10.1126/science.adu8578 Strengthening nucleic acid biosecurity screening against generative protein design tools]
  
 
===Medicine===
 
===Medicine===

Latest revision as of 10:28, 3 October 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

See Also

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