Difference between revisions of "Science Agents"

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==Literature==
 
==Literature==
 
* [https://www.alphaxiv.org/explore alphaXiv | Explore]: Understand arXiv papers
 
* [https://www.alphaxiv.org/explore alphaXiv | Explore]: Understand arXiv papers
 +
* 2026-02: [https://www.nature.com/articles/s41586-025-10072-4 Synthesizing scientific literature with retrieval-augmented language models]
  
 
===LLM extract data from papers===
 
===LLM extract data from papers===
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* 2024-02: [https://arxiv.org/abs/2408.07055 LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs] ([https://github.com/THUDM/LongWriter code])
 
* 2024-02: [https://arxiv.org/abs/2408.07055 LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs] ([https://github.com/THUDM/LongWriter code])
 
* 2024-08: Scientific papers: [https://arxiv.org/abs/2408.06292 The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery]
 
* 2024-08: Scientific papers: [https://arxiv.org/abs/2408.06292 The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery]
 +
** 2026-04: [https://www.nature.com/articles/s41586-026-10265-5 Towards end-to-end automation of AI research]
 
* 2024-09: PaperQA2: [https://paper.wikicrow.ai/ Language Models Achieve Superhuman Synthesis of Scientific Knowledge] ([https://x.com/SGRodriques/status/1833908643856818443 𝕏 post], [https://github.com/Future-House/paper-qa code])
 
* 2024-09: PaperQA2: [https://paper.wikicrow.ai/ Language Models Achieve Superhuman Synthesis of Scientific Knowledge] ([https://x.com/SGRodriques/status/1833908643856818443 𝕏 post], [https://github.com/Future-House/paper-qa code])
 
* 2025-03: [https://arxiv.org/abs/2503.18866 Reasoning to Learn from Latent Thoughts]
 
* 2025-03: [https://arxiv.org/abs/2503.18866 Reasoning to Learn from Latent Thoughts]
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* 2025-06: [https://arxiv.org/abs/2506.00794 Predicting Empirical AI Research Outcomes with Language Models]
 
* 2025-06: [https://arxiv.org/abs/2506.00794 Predicting Empirical AI Research Outcomes with Language Models]
 
* 2025-06: [https://arxiv.org/abs/2506.20803 The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas]
 
* 2025-06: [https://arxiv.org/abs/2506.20803 The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas]
 +
* 2026-03: [https://arxiv.org/abs/2603.14473 AI Can Learn Scientific Taste]
  
 
==Adapting LLMs to Science==
 
==Adapting LLMs to Science==
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===Science Foundation Models===
 
===Science Foundation Models===
 
* 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://arxiv.org/abs/2511.15684 Walrus: A Cross-Domain Foundation Model for Continuum Dynamics]
 +
* 2026-01: [https://www.science.org/doi/10.1126/science.ads9530 Deep contrastive learning enables genome-wide virtual screening]
  
 
===Regression (Data Fitting)===
 
===Regression (Data Fitting)===
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* [https://github.com/TheBlewish/Automated-AI-Web-Researcher-Ollama Automated-AI-Web-Researcher-Ollama]
 
* [https://github.com/TheBlewish/Automated-AI-Web-Researcher-Ollama Automated-AI-Web-Researcher-Ollama]
 
* 2025-01: [https://arxiv.org/abs/2501.05366 Search-o1: Agentic Search-Enhanced Large Reasoning Models] ([https://search-o1.github.io/ project], [https://github.com/sunnynexus/Search-o1 code])
 
* 2025-01: [https://arxiv.org/abs/2501.05366 Search-o1: Agentic Search-Enhanced Large Reasoning Models] ([https://search-o1.github.io/ project], [https://github.com/sunnynexus/Search-o1 code])
 +
* 2026-02: [https://www.nature.com/articles/s41586-025-10072-4 Synthesizing scientific literature with retrieval-augmented language models] ([https://allenai.org/blog/openscholar-nature blog])
  
 
===Commercial===
 
===Commercial===
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* [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])
 
* [https://periodic.com/ Periodic Labs]
 
* [https://periodic.com/ Periodic Labs]
 +
* [https://edisonscientific.com/articles/announcing-edison-scientific Edison Scientific] (drug discovery, spinoff from [https://www.futurehouse.org/ FutureHouse])
 +
* 2026-03: Mirendil Inc.: advanced models to speed up R&D in scientific domains, especially biology and materials science
  
 
====Bio====
 
====Bio====
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===Materials===
 
===Materials===
−
* 2024-12: [https://www.nature.com/articles/s41467-024-54639-7 Crystal structure generation with autoregressive large language modeling
+
* 2024-12: [https://www.nature.com/articles/s41467-024-54639-7 Crystal structure generation with autoregressive large language modeling]
 
* 2025-03: [https://arxiv.org/abs/2503.03965 All-atom Diffusion Transformers: Unified generative modelling of molecules and materials]
 
* 2025-03: [https://arxiv.org/abs/2503.03965 All-atom Diffusion Transformers: Unified generative modelling of molecules and materials]
 +
* 2022-11: [https://arxiv.org/abs/2511.19730 Training-Free Active Learning Framework in Materials Science with Large Language Models]
  
 
===Chemistry===
 
===Chemistry===
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* 2025-04: [https://arxiv.org/abs/2504.08051 Compositional Flows for 3D Molecule and Synthesis Pathway Co-design]
 
* 2025-04: [https://arxiv.org/abs/2504.08051 Compositional Flows for 3D Molecule and Synthesis Pathway Co-design]
 
* 2025-07: [https://arxiv.org/abs/2507.07456 General purpose models for the chemical sciences]
 
* 2025-07: [https://arxiv.org/abs/2507.07456 General purpose models for the chemical sciences]
 +
* 2025-11: [https://chemrxiv.org/engage/chemrxiv/article-details/690357d9a482cba122e366b6 ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models]
  
 
===Biology===
 
===Biology===
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* 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]
 
* 2025-09: [https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 Generative design of novel bacteriophages with genome language models]
 
* 2025-09: [https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 Generative design of novel bacteriophages with genome language models]
 +
* 2025-10: [https://www.science.org/doi/10.1126/science.adu8578 Strengthening nucleic acid biosecurity screening against generative protein design tools]
 +
* 2026-01: [https://www.nature.com/articles/s41586-025-10014-0 Advancing regulatory variant effect prediction with AlphaGenome]
  
 
===Medicine===
 
===Medicine===
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* 2025-02: [https://www.biorxiv.org/content/10.1101/2025.02.06.636901v1 From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models]
 
* 2025-02: [https://www.biorxiv.org/content/10.1101/2025.02.06.636901v1 From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models]
 
* 2025-02: [https://www.goodfire.ai/blog/interpreting-evo-2 Interpreting Evo 2: Arc Institute's Next-Generation Genomic Foundation Model]
 
* 2025-02: [https://www.goodfire.ai/blog/interpreting-evo-2 Interpreting Evo 2: Arc Institute's Next-Generation Genomic Foundation Model]
 +
* 2026-01: [https://www.goodfire.ai/research/interpretability-for-alzheimers-detection# Using Interpretability to Identify a Novel Class of Alzheimer's Biomarkers]
  
 
===Uncertainty===
 
===Uncertainty===
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** 2024-07: [https://arxiv.org/abs/2407.09413 SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers]
 
** 2024-07: [https://arxiv.org/abs/2407.09413 SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers]
 
** 2024-10: [https://neurips.cc/virtual/2024/98540 FEABench: Evaluating Language Models on Real World Physics Reasoning Ability]
 
** 2024-10: [https://neurips.cc/virtual/2024/98540 FEABench: Evaluating Language Models on Real World Physics Reasoning Ability]
 +
* 2026-02: [https://edisonscientific.com/ Edison]: [https://lab-bench.ai/ LABBench 2]
  
 
=Science Agents=
 
=Science Agents=
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* 2025-07: [https://arxiv.org/abs/2507.01903 AI4Research: A Survey of Artificial Intelligence for Scientific Research]
 
* 2025-07: [https://arxiv.org/abs/2507.01903 AI4Research: A Survey of Artificial Intelligence for Scientific Research]
 
* 2025-08: [https://arxiv.org/abs/2508.14111 From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery]
 
* 2025-08: [https://arxiv.org/abs/2508.14111 From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery]
 +
 +
==Challenges==
 +
* 2026-01: [https://arxiv.org/abs/2601.03315 Why LLMs Aren't Scientists Yet: Lessons from Four Autonomous Research Attempts]
  
 
==Specific==
 
==Specific==
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* 2025-04-08: Sakana: [https://pub.sakana.ai/ai-scientist-v2/paper/paper.pdf The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search] ([https://github.com/SakanaAI/AI-Scientist-v2 code])
 
* 2025-04-08: Sakana: [https://pub.sakana.ai/ai-scientist-v2/paper/paper.pdf The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search] ([https://github.com/SakanaAI/AI-Scientist-v2 code])
 
* 2025-07: [https://arxiv.org/abs/2507.14267 DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation]
 
* 2025-07: [https://arxiv.org/abs/2507.14267 DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation]
 +
* 2025-11: [https://arxiv.org/abs/2511.02824 Kosmos: An AI Scientist for Autonomous Discovery]
 +
* 2025-11: [https://arxiv.org/abs/2511.08151 SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning]
 +
* 2026-02: [https://arxiv.org/abs/2601.23265 PaperBanana: Automating Academic Illustration for AI Scientists]
 +
* 2026-03: [https://arxiv.org/abs/2603.20179 AI Agents Can Already Autonomously Perform Experimental High Energy Physics]
  
 
==Science Multi-Agent Setups==
 
==Science Multi-Agent Setups==
 
* 2025-01: [https://arxiv.org/abs/2501.04227 Agent Laboratory: Using LLM Agents as Research Assistants]
 
* 2025-01: [https://arxiv.org/abs/2501.04227 Agent Laboratory: Using LLM Agents as Research Assistants]
 
* 2025-04: [https://www.nature.com/articles/s41551-025-01363-2 Coordinated AI agents for advancing healthcare] ([https://www.nature.com/articles/s41551-025-01363-2.epdf?sharing_token=CIYP3J8LZE4BX31fV3WxUdRgN0jAjWel9jnR3ZoTv0O9iD-yhgqzRaz_7VASayWRePPhWDD2xFyfuOpSXbdPaOtt7oH4nfXo7telALzNwY3V1p9SxoqBEJy2OuaJ_cA35-CYQC1XgjCNTZUw46dh1KX-Dj8e7-1Vk_RlZKFLrc8%3D pdf])
 
* 2025-04: [https://www.nature.com/articles/s41551-025-01363-2 Coordinated AI agents for advancing healthcare] ([https://www.nature.com/articles/s41551-025-01363-2.epdf?sharing_token=CIYP3J8LZE4BX31fV3WxUdRgN0jAjWel9jnR3ZoTv0O9iD-yhgqzRaz_7VASayWRePPhWDD2xFyfuOpSXbdPaOtt7oH4nfXo7telALzNwY3V1p9SxoqBEJy2OuaJ_cA35-CYQC1XgjCNTZUw46dh1KX-Dj8e7-1Vk_RlZKFLrc8%3D pdf])
 +
 +
=Science Agentic Components=
 +
==Frameworks==
 +
* [https://platform.claude.com/docs/en/agent-sdk/overview Anthropic Claude Agent SKD overview]
 +
* [https://openclaw.ai/ OpenClaw]
 +
* [https://opencode.ai/ OpenCode]
 +
* [https://github.com/OpenHands/software-agent-sdk OpenHands]
 +
* [https://github.com/lamm-mit?tab=repositories LAMM: MIT Laboratory for Atomistic and Molecular Mechanics]
 +
** [https://github.com/lamm-mit/scienceclaw ScienceClaw]: Framework for autonomous scientific investigation without central coordination.
 +
** [https://infinite-lamm.vercel.app/ Infinite]: The Infinite Corridor of Scientific Discovery. Open science, powered by many — agents and humans discovering together.
 +
 +
==Personalities==
 +
* 2026-03: [https://github.com/msitarzewski/agency-agents The Agency: AI Specialists Ready to Transform Your Workflow]
 +
 +
==Skills==
 +
* 2026-03: [https://github.com/K-Dense-AI/claude-scientific-skills/tree/main?tab=readme-ov-file#use-cases Claude Scientific Skills] (list)
  
 
=AI Science Systems=
 
=AI Science Systems=
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** 2025-07: [https://arxiv.org/abs/2507.00964 Benchmarking the Discovery Engine] ([https://www.leap-labs.com/blog/how-we-replicated-five-peer-reviewed-papers-in-five-hours blog])
 
** 2025-07: [https://arxiv.org/abs/2507.00964 Benchmarking the Discovery Engine] ([https://www.leap-labs.com/blog/how-we-replicated-five-peer-reviewed-papers-in-five-hours blog])
 
* 2025-07: [https://www.preprints.org/manuscript/202507.1951/v1 Autonomous Scientific Discovery Through Hierarchical AI Scientist Systems]
 
* 2025-07: [https://www.preprints.org/manuscript/202507.1951/v1 Autonomous Scientific Discovery Through Hierarchical AI Scientist Systems]
 +
* 2025-12: [https://arxiv.org/abs/2512.16969 Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows]
 +
* 2026-01: [https://www.nature.com/articles/s43588-025-00906-6 SciSciGPT: advancing human–AI collaboration in the science of science]
 +
* 2026-02: [https://allenai.org/papers/autodiscovery AUTODISCOVERY: Open-ended Scientific Discovery via Bayesian Surprise] (Allen AI (Ai2) AstraLabs, [https://allenai.org/blog/autodiscovery blog], [https://autodiscovery.allen.ai/runs tools])
  
 
===Inorganic Materials Discovery===
 
===Inorganic Materials Discovery===
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===Bio===
 
===Bio===
 
* 2025-07: [https://arxiv.org/abs/2507.01485 BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experiments]
 
* 2025-07: [https://arxiv.org/abs/2507.01485 BioMARS: A Multi-Agent Robotic System for Autonomous Biological Experiments]
 +
 +
===Physics===
 +
* 2025-12: [https://arxiv.org/abs/2512.19799 PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research]
  
 
==LLMs Optimized for Science==
 
==LLMs Optimized for Science==
 
* 2022-11: [https://arxiv.org/abs/2211.09085 Galactica: A Large Language Model for Science]
 
* 2022-11: [https://arxiv.org/abs/2211.09085 Galactica: A Large Language Model for Science]
−
* 2024-12: [https://www.nature.com/articles/s41467-024-54639-7 Crystal structure generation with autoregressive large language modeling
+
* 2024-12: [https://www.nature.com/articles/s41467-024-54639-7 Crystal structure generation with autoregressive large language modeling]
 
* 2025-02: [https://arxiv.org/abs/2502.13107 MatterChat: A Multi-Modal LLM for Material Science]
 
* 2025-02: [https://arxiv.org/abs/2502.13107 MatterChat: A Multi-Modal LLM for Material Science]
 
* 2025-03: [https://arxiv.org/abs/2503.17604 OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery]
 
* 2025-03: [https://arxiv.org/abs/2503.17604 OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery]
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** 2025-05: Retraction: [https://economics.mit.edu/news/assuring-accurate-research-record Assuring an accurate research record]
 
** 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]
 +
* 2026-02: [https://arxiv.org/abs/2602.03837 Accelerating Scientific Research with Gemini: Case Studies and Common Techniques]
  
 
=Related Tools=
 
=Related Tools=
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* [https://github.com/blaiszik/awesome-matchem-datasets/ Awesome Materials & Chemistry Datasets]
 
* [https://github.com/blaiszik/awesome-matchem-datasets/ Awesome Materials & Chemistry Datasets]
 
* NIST [https://jarvis.nist.gov/ Jarvis] (simulations)
 
* NIST [https://jarvis.nist.gov/ Jarvis] (simulations)
 +
 +
=Genuine Discoveries=
 +
* 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]
 +
* 2025-12: [https://andymasley.substack.com/p/ai-can-obviously-create-new-knowledge AI can obviously create new knowledge - But maybe not new concepts]
 +
==Math==
 +
* 2023-07: [https://www.nature.com/articles/s41586-023-06004-9?utm_source=chatgpt.com Faster sorting algorithms discovered using deep reinforcement learning]
 +
* 2025-06: [https://arxiv.org/abs/2506.13131 AlphaEvolve: A coding agent for scientific and algorithmic discovery]
 +
* 2025-11: [https://arxiv.org/abs/2511.02864 Mathematical exploration and discovery at scale]
 +
* 2025-11: [https://www.nature.com/articles/s41586-025-09833-y Olympiad-level formal mathematical reasoning with reinforcement learning]
 +
* 2025-12: [https://arxiv.org/abs/2512.14575 Extremal descendant integrals on moduli spaces of curves: An inequality discovered and proved in collaboration with AI]
 +
* [https://github.com/teorth/erdosproblems/wiki/AI-contributions-to-Erd%C5%91s-problems AI Solving Erdős Problems]:
 +
** 2026-01: [https://www.erdosproblems.com/728 Erdős Problem #728] and [https://www.erdosproblems.com/729 #729] solved by Aristotle using ChatGPT 5.2 Pro
 +
** 2026-01: [https://www.erdosproblems.com/forum/thread/397 Erdős Problem #397] [https://x.com/neelsomani/status/2010215162146607128?s=20 solved] by [https://neelsomani.com/ Neel Somani] using ChatGPT 5.2 Pro
 +
** 2026-01: [https://www.erdosproblems.com/205 Erdős Problem #205] solved by Aristotle using ChatGPT 5.2 Pro
 +
** 2026-01: [https://www.erdosproblems.com/forum/thread/281 Erdős Problem #281] [https://x.com/neelsomani/status/2012695714187325745?s=20 solved] by [https://neelsomani.com/ Neel Somani] using ChatGPT 5.2 Pro
 +
** 2026-01: Google DeepMind: [https://arxiv.org/abs/2601.21442 Irrationality of rapidly converging series: a problem of Erdős and Graham]
 +
*** [https://www.erdosproblems.com/1051 Erdős Problem #1051] [https://x.com/slow_developer/status/2018321002623901885?s=20 solved] by Google DeepMind Aletheia agent
 +
** 2026-01: Google DeepMind: [https://arxiv.org/abs/2601.22401 Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erdős Problems]
 +
*** Attempted 700 problems, solved 13 open Erdős problems: 5 novel autonomous solutions, 8 through existing literature.
 +
** 2026-02: [https://www.erdosproblems.com/846 Erdős Problem #846]
 +
*** [https://x.com/roydanroy/status/2026804567178953048?s=20 Google DeepMind]
 +
*** [https://x.com/mehtaab_sawhney/status/2026716221933343147?s=20 Using OpenAI internal model] (paper: [https://cdn.openai.com/infinite-sets/main_single_clean3.pdf On infinite sets with no 3 on a line])
 +
** 2026-03: Three problems solved using OpenAI GPT internal model. Paper: [https://arxiv.org/pdf/2603.29961 Short Proofs in Combinatorics and Number Theory]
 +
* 2026-01: [https://arxiv.org/abs/2601.07222 The motivic class of the space of genus 0 maps to the flag variety]
 +
* 2026-02: Google DeepMind: [https://arxiv.org/abs/2602.10177 Towards Autonomous Mathematics Research]
 +
* 2026-03: Donald Knuth: [https://www-cs-faculty.stanford.edu/~knuth/papers/claude-cycles.pdf A problem in Directed Hamiltonian Cycles] solved by Filip Stappers using Claude Opus 4.6
 +
* 2026-03: Google DeepMind: [https://arxiv.org/abs/2603.09172 Reinforced Generation of Combinatorial Structures: Ramsey Numbers]
 +
* 2026-03: [https://epoch.ai/frontiermath/open-problems FrontierMath] problem: [https://epoch.ai/frontiermath/open-problems/ramsey-hypergraphs "A Ramsey-style Problem on Hypergraphs"] solved by Kevin Barreto and Liam Price using GPT-5.4 Pro
 +
 +
==Physics assistance==
 +
* 2025-03: [https://arxiv.org/abs/2503.23758 Exact solution of the frustrated Potts model with next-nearest-neighbor interactions in one dimension via AI bootstrapping] ([https://www.bnl.gov/staff/wyin Weiguo Yin])
 +
* 2025-12: [https://www.sciencedirect.com/science/article/pii/S0370269325008111 Relativistic covariance and nonlinear quantum mechanics: Tomonaga-Schwinger analysis]
 +
** [https://x.com/hsu_steve/status/1996034522308026435?s=20 Steve Hsu], [https://drive.google.com/file/d/16sxJuwsHoi-fvTFbri9Bu8B9bqA6lr1H/view Theoretical Physics with Generative AI]
 +
* 2026-02: [https://arxiv.org/abs/2602.12176 Single-minus gluon tree amplitudes are nonzero] (GPT-5.2, [https://openai.com/index/new-result-theoretical-physics/ blog])
 +
 +
==Literature exploration==
 +
* 2025-11: [https://arxiv.org/abs/2511.02824 Kosmos: An AI Scientist for Autonomous Discovery] ([https://edisonscientific.com/ Edison])
 +
** [https://platform.edisonscientific.com/kosmos/c4bdef64-5e9b-43b9-a365-592dd1ed7587 Nucleotide metabolism in hypothermia]
 +
** [https://platform.edisonscientific.com/kosmos/1fdbf827-be65-4d97-9b66-bf0da600091a Determinant of perovskite solar-cell failure]
 +
** [https://platform.edisonscientific.com/kosmos/4fb3fbdb-c449-4064-9aa6-ff4ec53131d8 Log-normal connectivity in neural networks]
 +
** [https://platform.edisonscientific.com/kosmos/c6849232-5858-4634-adf5-83780afbe3db SOD2 as driver of myocardial fibrosis]
 +
** [https://platform.edisonscientific.com/kosmos/abac07da-a6bb-458f-b0ba-ef08f1be617e Protective variant of SSR1 in type 2 diabetes]
 +
** [https://platform.edisonscientific.com/kosmos/a770052b-2334-4bbe-b086-5149e0f03d99 Temporal ordering in Alzheimer’s disease]
 +
** [https://platform.edisonscientific.com/kosmos/28c427d2-be31-48b5-b272-28d5a1e3ea5c Mechanism of neuron vulnerability in aging]
 +
==Bio design==
 +
* 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://deepmind.google/blog/alphafold-five-years-of-impact/ AlphaFold: Five years of impact]
 +
* 2026-01: [https://www.goodfire.ai/research/interpretability-for-alzheimers-detection# Using Interpretability to Identify a Novel Class of Alzheimer's Biomarkers]
 +
==Material Discovery==
 +
* 2023-11: [https://doi.org/10.1038/s41586-023-06735-9 Scaling deep learning for materials discovery]
  
 
=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 09:40, 2 April 2026

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

Challenges

Specific

Science Multi-Agent Setups

Science Agentic Components

Frameworks

Personalities

Skills

AI Science Systems

Inorganic Materials Discovery

Materials Characterization

Chemistry

Bio

Physics

LLMs Optimized for Science

Impact of AI in Science

Related Tools

Literature Search

Data Visualization

Generative

Chemistry

Science Datasets

Genuine Discoveries

Math

Physics assistance

Literature exploration

Bio design

Material Discovery

See Also

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