Thanks to the advances in computational power, automated systems have been combined with machine learning (ML) techniques and applied in discovery of novel materials and reactions , process development and optimization . Automated systems mainly consist of three components: a decision-making algorithm, a physical platform capable of undertaking the desired class of experiments autonomously and an automated inline/online analytical setup . As the 'brain' of automated platforms, the decision algorithm plays a vital role. On this basis, ML algorithms based on statistical surrogate modelling approaches have become increasingly popular, having been shown that they provide accurate models for predictions .