Title

Autonomous Lab Agent for Experimental Design

Description

Design and evaluate a reinforcement learning (RL) or planning-based agent capable of optimizing experimental decisions in a simulated laboratory environment. The goal is to emulate aspects of autonomous scientific discovery by developing an agent that selects actions (e.g., experimental conditions or measurement sequences) to maximize information gain or target outcome efficiency. The project will focus on proof-of-concept systems, such as toy models of chemical or biological processes, where the agent learns optimal strategies for experiment design.

Skills Required

• Reinforcement learning fundamentals
• Basic probability and decision theory
• Familiarity with OpenAI Gym or similar simulation environments

Skills to be Developed

• Experimental design and active learning
• Policy learning and model-based RL
• Integration of physical process knowledge into AI systems
• Reproducible simulation pipelines for scientific domains

Relevant Background Reading

1. [Gómez-Bombarelli et al., 2018] Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
2. [Schneider et al., 2020] Autonomous Experimentation Systems for Materials Development: A Community Perspective