A Simple Yet Powerful Hybrid Machine Learning Approach to Aid Decision-Making in Laboratory Experiments

High-dimensional experimental spaces and resource constraints challenge modern science. We introduce a hybrid machine-learning (ML) framework that combines Ordinary Least Squares (OLS) for global surface estimation, Gaussian Process (GP) regression for uncertainty modelling, expected improvement (EI) for active learning, and K-means clustering for diversifying conditions. We applied this approach to published growth-rate data of the diatom Thalassiosira pseudonana, originally measured across 25 phosphate–temperature conditions. Using the nutrient–temperature model as a simulator, our ML framework located the optimal growth conditions in only 25 virtual experiments—matching the original study’s outcome. Sensitivity analyses further revealed that fewer iterations and controlled batch sizes maintain accuracy even with higher data variability. This demonstrates that ML-guided experimentation can achieve expert-level decision-making without extensive prior data, reducing experimental burden while preserving rigour. Our results highlight the promise of algorithm-assisted experimentation in biology, agriculture, and medicine, marking a shift toward smarter, data-driven scientific workflow