About Workshop
This workshop introduces participants to autonomous scientific discovery using multi-agent AI systems, foundation models, and self-driving research labs. It highlights how AI can support literature review, hypothesis generation, experiment planning, data analysis, and research automation across scientific domains.
Aim
The aim of this workshop is to help participants understand how autonomous AI systems can support and accelerate scientific research by combining multi-agent intelligence, foundation models, automated experimentation, and self-driving laboratory workflows.
What Participants Will Learn
- Understand the concept of autonomous scientific discovery.
- Explore the role of multi-agent AI scientists in research workflows.
- Learn how foundation models support scientific reasoning and experimentation.
- Understand the basics of self-driving research labs.
- Identify applications in biotechnology, drug discovery, nanotechnology, materials science, and sustainability.
- Recognize key challenges such as reliability, ethics, and reproducibility.
Structure
📅 Day 1: Foundations of Autonomous Scientific Discovery
Topics Covered
- Introduction to autonomous scientific discovery
- From traditional research workflows to AI-powered research workflows
- Role of foundation models in scientific reasoning
- AI co-scientists and research assistants
- Literature mining, research gap identification, and hypothesis generation
- Applications in biotechnology, drug discovery, nanotechnology, materials science, and sustainability
Hands-on Activity
Google Colab Activity: Build a simple AI-powered literature insight workflow using Python to analyze research abstracts and extract keywords, research themes, and possible hypothesis ideas.
📅 Day 2: Multi-Agent AI Scientists for Research Automation
Topics Covered
- What are multi-agent AI systems?
- Role-based AI agents: literature agent, hypothesis agent, experiment planner, data analyst, and reviewer agent
- Agentic workflows for scientific problem-solving
- Prompt engineering for research agents
- Tool-using AI agents for databases, papers, code, and scientific analysis
- Reliability, validation, and human-in-the-loop research supervision
Hands-on Activity
Google Colab Activity: Create a basic multi-agent research workflow where different AI agents perform literature summarization, hypothesis generation, and experiment planning for a selected research topic.
📅 Day 3: Self-Driving Research Labs and Future of AI-Driven Science
Topics Covered
- Introduction to self-driving laboratories
- AI-driven experimental design and optimization
- Closed-loop research: plan, test, analyze, improve
- Use of machine learning for experiment recommendation
- Applications in chemistry, materials discovery, pharma, biotech, and energy research
- Ethical, reproducibility, safety, and governance challenges
- Future career and research opportunities in autonomous science
Hands-on Activity
Google Colab Activity: Simulate a self-driving lab workflow using a sample dataset where an AI model recommends the next best experiment based on previous experimental results.
Important Dates
Registration Ends
4: 30 PM IST
Workshop Dates
2026-06-15
05:30 PM IST
05:30 PM IST
What You Will Gain
Outcomes
- Explain how AI is transforming scientific discovery.
- Describe the use of AI agents and foundation models in research.
- Understand how self-driving labs automate experimentation.
- Identify real-world applications of autonomous research systems.
- Evaluate the benefits and limitations of AI-powered scientific workflows.