Difference between revisions of "Science Agents"
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===Biology=== | ===Biology=== | ||
| − | * 2025-01: [https://www.nature.com/articles/s41586-024-08435-4 Targeting protein–ligand neosurfaces with a generalizable deep learning tool] | + | * 2018: [https://alphafold.ebi.ac.uk/ AlphaFold] |
| + | * 2021-07: [https://www.nature.com/articles/s41586-021-03819-2 AlphaFold 2] | ||
| + | * 2024-05: [https://www.nature.com/articles/s41586-024-07487-w AlphaFold 3] | ||
| + | * 2023-03: [https://www.science.org/doi/10.1126/science.ade2574 Evolutionary-scale prediction of atomic-level protein structure with a language model] ([https://esmatlas.com/resources?action=fold ESMFold]) | ||
| + | * 2023-11: [https://www.nature.com/articles/s41586-023-06728-8 Illuminating protein space with a programmable generative model] | ||
| + | * 2024-11: [https://www.science.org/doi/10.1126/science.ado9336 Sequence modeling and design from molecular to genome scale with Evo] (Evo) | ||
| + | * 2025-01: [https://www.nature.com/articles/s41586-024-08435-4 Targeting protein–ligand neosurfaces with a generalizable deep learning tool] (Chroma) | ||
| + | * 2025-01: [https://www.science.org/doi/10.1126/science.ads0018 Simulating 500 million years of evolution with a language model] ([https://github.com/evolutionaryscale/esm ESM] 3 model) | ||
| + | * 2025-02: [https://arcinstitute.org/manuscripts/Evo2 Genome modeling and design across all domains of life with Evo 2] | ||
| + | * 2025-02: [https://www.microsoft.com/en-us/research/blog/exploring-the-structural-changes-driving-protein-function-with-bioemu-1/ Exploring the structural changes driving protein function with BioEmu-1] | ||
==AI/ML Methods co-opted for Science== | ==AI/ML Methods co-opted for Science== | ||
| Line 86: | Line 95: | ||
* 2024-10-28: [https://arxiv.org/abs/2410.20976 Large Language Model-Guided Prediction Toward Quantum Materials Synthesis] | * 2024-10-28: [https://arxiv.org/abs/2410.20976 Large Language Model-Guided Prediction Toward Quantum Materials Synthesis] | ||
* 2024-12-06: [https://www.biorxiv.org/content/10.1101/2024.11.11.623004v1 The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation] (writeup: [https://www.nature.com/articles/d41586-024-01684-3 Virtual lab powered by ‘AI scientists’ super-charges biomedical research: Could human–AI collaborations be the future of interdisciplinary studies?]) | * 2024-12-06: [https://www.biorxiv.org/content/10.1101/2024.11.11.623004v1 The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation] (writeup: [https://www.nature.com/articles/d41586-024-01684-3 Virtual lab powered by ‘AI scientists’ super-charges biomedical research: Could human–AI collaborations be the future of interdisciplinary studies?]) | ||
| − | |||
* 2024-12-30: [https://arxiv.org/abs/2412.21154 Aviary: training language agents on challenging scientific tasks] | * 2024-12-30: [https://arxiv.org/abs/2412.21154 Aviary: training language agents on challenging scientific tasks] | ||
| + | * See also: [[AI_Agents#Deep_Research|AI Agents > Deep Research]] | ||
==Science Multi-Agent Setups== | ==Science Multi-Agent Setups== | ||
| Line 94: | Line 103: | ||
=AI Science Systems= | =AI Science Systems= | ||
* 2025-01: [https://arxiv.org/abs/2501.03916 Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback] | * 2025-01: [https://arxiv.org/abs/2501.03916 Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback] | ||
| + | * 2025-02: [https://storage.googleapis.com/coscientist_paper/ai_coscientist.pdf Towards an AI co-scientist] (Google blog post: [https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/ Accelerating scientific breakthroughs with an AI co-scientist]) | ||
===Inorganic Materials Discovery=== | ===Inorganic Materials Discovery=== | ||
| Line 99: | Line 109: | ||
* 2023-11: [https://doi.org/10.1038/s41586-023-06734-w An autonomous laboratory for the accelerated synthesis of novel materials] | * 2023-11: [https://doi.org/10.1038/s41586-023-06734-w An autonomous laboratory for the accelerated synthesis of novel materials] | ||
* 2024-10: [https://arxiv.org/abs/2410.12771 Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models] ([https://github.com/FAIR-Chem/fairchem code], [https://huggingface.co/datasets/fairchem/OMAT24 datasets], [https://huggingface.co/fairchem/OMAT24 checkpoints], [https://ai.meta.com/blog/fair-news-segment-anything-2-1-meta-spirit-lm-layer-skip-salsa-sona/ blogpost]) | * 2024-10: [https://arxiv.org/abs/2410.12771 Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models] ([https://github.com/FAIR-Chem/fairchem code], [https://huggingface.co/datasets/fairchem/OMAT24 datasets], [https://huggingface.co/fairchem/OMAT24 checkpoints], [https://ai.meta.com/blog/fair-news-segment-anything-2-1-meta-spirit-lm-layer-skip-salsa-sona/ blogpost]) | ||
| + | * 2025-01: [https://www.nature.com/articles/s41586-025-08628-5 A generative model for inorganic materials design] | ||
===Chemistry=== | ===Chemistry=== | ||
Latest revision as of 12:23, 20 February 2025
Contents
- 1 AI Use-cases for Science
- 2 Science Benchmarks
- 3 Science Agents
- 4 AI Science Systems
- 5 Impact of AI in Science
- 6 Related Tools
- 7 See Also
AI Use-cases for Science
Literature
LLM extract data from papers
AI finding links in literature
- 2019-07: Unsupervised word embeddings capture latent knowledge from materials science literature
- 2024-11: Large language models surpass human experts in predicting neuroscience results
Autonomous Ideation
- 2024-09: Mining Causality: AI-Assisted Search for Instrumental Variables
- 2024-12: Thinking Fast and Laterally: Multi-Agentic Approach for Reasoning about Uncertain Emerging Events
- 2024-12: LLMs can realize combinatorial creativity: generating creative ideas via LLMs for scientific research
- 2024-12: LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context
Adapting LLMs to Science
- 2023-06: Domain-specific chatbots for science using embeddings
- 2024-10: Personalization of Large Language Models: A Survey
- 2024-11: Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
AI/ML Methods tailored to Science
Regression (Data Fitting)
- 2024-06: 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)
- 2024-12: OmniPred: Language Models as Universal Regressors
Tabular Classification/Regression
Symbolic Regression
Literature Discovery
- FutureHouse
- Lumina
- Automated-AI-Web-Researcher-Ollama
- 2025-01: Search-o1: Agentic Search-Enhanced Large Reasoning Models (project, code)
Commercial
- Cusp AI: Materials/AI
AI/ML Methods in Science
Chemistry
Biology
- 2018: AlphaFold
- 2021-07: AlphaFold 2
- 2024-05: AlphaFold 3
- 2023-03: Evolutionary-scale prediction of atomic-level protein structure with a language model (ESMFold)
- 2023-11: Illuminating protein space with a programmable generative model
- 2024-11: Sequence modeling and design from molecular to genome scale with Evo (Evo)
- 2025-01: Targeting protein–ligand neosurfaces with a generalizable deep learning tool (Chroma)
- 2025-01: Simulating 500 million years of evolution with a language model (ESM 3 model)
- 2025-02: Genome modeling and design across all domains of life with Evo 2
- 2025-02: Exploring the structural changes driving protein function with BioEmu-1
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.
- Mechanistic interpretability for protein language models (visualizer, code, SAE)
- Markov Bio: Through a Glass Darkly: Mechanistic Interpretability as the Bridge to End-to-End Biology (quick description, background info on recent bio progress)
- 2023-01: Tracr: Compiled Transformers as a Laboratory for Interpretability (code)
- 2024-12: Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
- 2024-12: InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders
- 2025-01: Insights on Galaxy Evolution from Interpretable Sparse Feature Networks
- 2025-02: From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models
Uncertainty
- 2024-10: entropix: Entropy Based Sampling and Parallel CoT Decoding
- 2024-10: Taming Overconfidence in LLMs: Reward Calibration in RLHF
Science Benchmarks
- 2024-07: SciCode: A Research Coding Benchmark Curated by Scientists (project)
- 2024-11: AidanBench: Evaluating Novel Idea Generation on Open-Ended Questions (code)
- 2024-12: LiveIdeaBench: Evaluating LLMs' Scientific Creativity and Idea Generation with Minimal Context
- 2025-01: Humanity's Last Exam
- ScienceAgentBench
Science Agents
Reviews
- 2024-10: Empowering biomedical discovery with AI agents
- 2025-01: A review of large language models and autonomous agents in chemistry (github)
Specific
- 2024-01-13: ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization (video)
- 2024-06-19: LLMatDesign: Autonomous Materials Discovery with Large Language Models
- 2024-08-12: Sakana AI: AI Scientist; The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (code)
- 2024-09-09: SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning (code)
- 2024-09-11: PaperQA2: Language Models Achieve Superhuman Synthesis of Scientific Knowledge (𝕏 post, code)
- 2024-10-17: Rapid and Automated Alloy Design with Graph Neural Network-Powered LLM-Driven Multi-Agent Systems
- 2024-10-28: Large Language Model-Guided Prediction Toward Quantum Materials Synthesis
- 2024-12-06: The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation (writeup: Virtual lab powered by ‘AI scientists’ super-charges biomedical research: Could human–AI collaborations be the future of interdisciplinary studies?)
- 2024-12-30: Aviary: training language agents on challenging scientific tasks
- See also: AI Agents > Deep Research
Science Multi-Agent Setups
AI Science Systems
- 2025-01: Dolphin: Closed-loop Open-ended Auto-research through Thinking, Practice, and Feedback
- 2025-02: Towards an AI co-scientist (Google blog post: Accelerating scientific breakthroughs with an AI co-scientist)
Inorganic Materials Discovery
- 2023-11: Scaling deep learning for materials discovery
- 2023-11: An autonomous laboratory for the accelerated synthesis of novel materials
- 2024-10: Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models (code, datasets, checkpoints, blogpost)
- 2025-01: A generative model for inorganic materials design
Chemistry
- 2023-12: Autonomous chemical research with large language models (Coscientist)
- 2024-11: An automatic end-to-end chemical synthesis development platform powered by large language models
- 2025-01: Large language models for reticular chemistry
Impact of AI in Science
- 2024-11: Artificial Intelligence, Scientific Discovery, and Product Innovation
- 2025-02: Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation
Related Tools
Literature Search
Data Visualization
- 2024-10: Data Formulator: Create Rich Visualization with AI iteratively (video, code)
- Julius AI: Analyze your data with computational AI
See Also
- AI agents
- Nanobot.chat: Intelligent AI for the labnetwork @ mtl.mit.edu forum
