Science Agents

AI Use-cases for Science

Literature

• alphaXiv | Explore: Understand arXiv papers

LLM extract data from papers

• 2024-14: From text to insight: large language models for chemical data extraction

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

Explanation

• TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding (preprint)

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
• 2025-02: Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks

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

• 2025-01: Accurate predictions on small data with a tabular foundation model (code)

Symbolic Regression

• 2024-09: Symbolic Regression with a Learned Concept Library

Literature Discovery

• FutureHouse
  • hasanyone
  • PaperQA2
• Lumina
• Automated-AI-Web-Researcher-Ollama
• 2025-01: Search-o1: Agentic Search-Enhanced Large Reasoning Models (project, code)

Commercial

• Sakana AI
• Cusp AI: Materials/AI
• Lila AI: Life sciences
• Radical AI: Material simulation/design
• Autoscience (Carl)

AI/ML Methods in Science

Chemistry

• 2025-01: Large language models for reticular chemistry
• 2025-02: Image-based generation for molecule design with SketchMol
• 2025-02: Large language models for scientific discovery in molecular property prediction
• 2025-03: Vant AI Neo-1: atomistic foundation model (small molecules, proteins, etc.)

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
• 2025-02: Protein Large Language Models: A Comprehensive Survey

Successes

• 2025-02: Site-Decorated Model for Unconventional Frustrated Magnets: Ultranarrow Phase Crossover and Spin Reversal Transition

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
• 2025-02: EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
• 2025-03: BixBench: Novel hypotheses (accept/reject)

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

• 2025-01: Agent Laboratory: Using LLM Agents as Research Assistants

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

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

• Perplexity
• ArXival

Data Visualization

• 2024-10: Microsoft Data Formulator: Create Rich Visualization with AI iteratively (video, code)
• Julius AI: Analyze your data with computational AI

Generative

• 2025-03: StarVector 1B, 8B: text or image to SVG

See Also

• AI agents
• Nanobot.chat: Intelligent AI for the labnetwork @ mtl.mit.edu forum