Abstract
This paper concerns chemical, biological and other experiments used in R&D labs, where the aim is to optimise some performance indicator by adjusting the materials and processing parameters. Such experiments require significant resources and time, motivating the use of early stopping strategies to terminate unpromising runs before completion. However, stopping an ongoing experiment based on incomplete observations carries the risk of incorrectly terminating runs that would have achieved satisfactory outcomes, a quantity we term the false stop rate (FSR). To address this, we propose Confidence-Bound Early Stopping with Sequential Calibration (CBES), a two-layer framework that employs Gaussian process regression to predict the final outcome from partial observations and combines a confidence-bound decision rule with a calibration procedure to ensure that the FSR remains below a user-specified level. We compare CBES against four baseline stopping criteria on two distinct domains: in vitro permeation testing (IVPT) for pharmaceutical formulation and LCBench for hyperparameter optimisation. The results demonstrate that CBES achieves reliable FSR control with substantial time savings. This work offers a flexible framework for experimental processes, with broad applicability in fields such as chemical engineering, biotechnology, and material science.
•A confidence-bound early stopping framework with sequential calibration (CBES) is proposed.•Gaussian process regression provides probabilistic predictions of final experimental outcomes.•Sequential calibration controls the false stop rate at a user-specified level without manual tuning.•A Hoeffding-based bound provides a formal generalisation guarantee on the false stop rate.•CBES achieves reliable false stop rate control with substantial time savings across two domains.