Sequential versus Manifold Bayesian Optimization under Realistic Experimental Time Constraints

TL;DR

This paper develops a time-aware Bayesian optimization framework that compares sequential and manifold strategies under realistic experimental constraints, guiding optimal choice in autonomous materials discovery.

Contribution

It introduces a novel time-normalized benchmarking method for BO strategies considering synthesis and characterization times, applicable to experimental workflows.

Findings

Sequential BO is optimal for short-term experiments.

Manifold BO outperforms when multiplexed synthesis accelerates data collection.

Transition between strategies depends on physically interpretable parameters.

Abstract

Bayesian optimization (BO) is widely used for autonomous materials discovery, yet its classical sequential formulation is insufficient for design of experimental workflows that often combine parallel or batch synthesis with inherently serial characterization. Methods such as combinatorial spread libraries and printed libraries sample a defined low-D manifold in the chemical space of the system. Here, we introduce a time-aware framework for comparing sequential and manifold BO under experimentally realistic constraints. By explicitly modeling synthesis and characterization times, we define an effective experimental time metric that enables fair, time-normalized benchmarking of optimization strategies. Using numerical experiments in ternary and quaternary compositional spaces, we show that sequential BO remains optimal for short-term experiments or when batching provides no effective time advantage, whereas manifold BO becomes favorable once multiplexed synthesis enables faster accumulation of measurements. We identify a small set of physically interpretable parameters that govern the transition between these regimes. These results establish a general, experimentally grounded framework for selecting optimization strategies in self-driving laboratories and autonomous materials discovery workflows. The accompanying analysis code is publicly available at https://github.com/Slautin/2025_GP_BO_Manifolds.