Multi-variable batch Bayesian optimization in materials research: Synthetic data analysis of noise sensitivity and problem landscape effects

Bayesian optimization (BO) holds promise for accelerating materials science research; however, it faces challenges with high-dimensional inputs and experimental noise in real-world problems. This study addresses these issues by benchmarking batch BO on two synthetic six-variable optimization tasks at varying noise levels: a needle-in-a-haystack task (Ackley function) representing rare materials properties, and a smooth landscape (Hartmann function) simulating process optimization. We evaluate key BO strategies, including acquisition functions, batch-picking methods, and exploration hyperparameter tuning, while presenting a framework for tracking high-dimensional optimization progress. Results show optimization outcomes are highly sensitive to noise levels and landscape shapes. This information enables the design of robust materials optimization campaigns with pre-planned experimental budgets that account for real-world uncertainties. Our methodology facilitates greater BO utilization in experimental materials research, particularly for multi-variable optimization problems, by providing practical guidance for configuring BO campaigns in challenging scientific applications.