Skip to content

Index

Generative AI has one of the most powerful potentials for science by enabling rapid-iteration closed-loop science-loop systems. A science loop system is one where measurements inform understanding in such a way to make better experiments and solutions.

    graph LR
    A[🛠️ Build<br>Experiments]:::blue --> B[🔬 Experiment<br>and Record]:::green
    B --> A
    B --> C[📏 Make into Measurements <br>to create Meaning]:::red
    C --> D[🔍 Analyze<br>for Meaning]:::yellow
    C --> B
    D --> C
    D --> E[🔮 Generate and Predict<br>New Experiments]:::purple
    E --> D
    E --> B
    E --> A

    classDef blue fill:#add8e6,stroke:#333,stroke-width:2px,color:black;
    classDef green fill:#98fb98,stroke:#333,stroke-width:2px,color:black;
    classDef red fill:#ffcccb,stroke:#333,stroke-width:2px,color:black;
    classDef yellow fill:#ffebcd,stroke:#333,stroke-width:2px,color:black;
    classDef purple fill:#dda0dd,stroke:#333,stroke-width:2px,color:black;

Research

📋
Link copied!
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models

Developments The authors demonstrate a LLM-enabled research agent to do several things: 

| "Research Idea Generation The goal of the research idea generation task is to formulate new
and valid research ideas, to enhance the overall efficiency of the first phase of scientific discovery,
which consists of three systematic steps: identifying problems, developing methods, and designing
experiments

image

They provide the following prompt to make this very useful. They can be seen in the site ./prompts/. We will make these viewable later.

Autonomous Science in the Loop

Science in the Loop Optimizaton enables for the creation and optimization of scientific-related components. Generally related to manual or semiautonomous autonomous biological, biochemistry, or chemistry laboratories, they may extend to other domains.

There are components of include

📋
Link copied!

GitHub Repo stars Autonomous chemical research with large language models

Developments The authors reveal how a 'Coscientist' architecture can assist in the development of more effective research results. image Paper Arxiv

Protocol Optimization

Getting protocols in usable manners is key. They must be usable by people, firstly, and then by more automated robotic systems.

Optimized protocols first need to start from having protocols. Protocols may start from those recorded in databases, or may be extracted from literature.

ProtoCode: Leveraging Large Language Models for Automated Generation of Machine-Readable Protocols from Scientific Publications

Developments The authors develop Protocode to finetune LLMs to convert protocols from literature into operational files for a thermal cycler system. image

Molecule Optimization

Molecule optimization focuses on the improvement of generally single component within a larger process. They can be simple molecules, as more complex bio-relevant molecules like drugs and biomolecules such as proteins and DNA.

Measurement Optimization

Measurement optimization involves improving the ability to measure something. This includes tuning physical parameters within a

Robotic automation

Autonomous laboratories are controlled by different robotics setups and automation languages including specific ones Lua or more general in-house control systems.

Risks to Consider

Like the use of GenAI in other domains, it is essential to consider the risks associated with its application, in this case to Science.

These risks can be considered quite generally, in the following categories 1. Incorrect output 2. Potentially, or likely, harmful output

We share information below related to understanding and safeguarding the application of LLMs and agents when applied in the scientific domain.

Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science

Developments: The authors present Vulnerabilities and solutions to the use of LLM Agents describing a triadic interaction between people, LLM agents, and environments. image