Role of new surrogate models, acquisition functions, and LLMs in self-driving labs

Determine the role and effectiveness of new surrogate models, acquisition functions, and large language models in overcoming cold-start limitations and enabling few-shot or zero-shot Bayesian optimization workflows in self-driving laboratory systems.

Background

Self-driving labs typically rely on Bayesian optimization to maximize data efficiency, yet cold-start scenarios and rigid workflows limit early-stage performance and sustainability. Leveraging prior knowledge, flexible function-network workflows, and improved representations for categorical design spaces are proposed to accelerate convergence.

The paper highlights uncertainty about how novel surrogate architectures, acquisition rules, and LLM-based tools will contribute to resolving these challenges, motivating targeted research and benchmarking in realistic experimental campaigns.