Hard carbons are currently the most promising candidates for anode materials in sodium-ion batteries (SIBs). [1] The disordered structure of hard carbons featuring high interlayer spacing, defects and pores provides suitable sites for sodium ion storage. The relationship between the structural features of glucose derived hard carbons and their electrochemical behaviour was previously reported. [2,3] The interplay of these features suggests there may be an optimal hard carbon microstructure which may result in improved electrochemical performance. Therefore, strategies for improving the electrochemical performance of hard carbons include tuning their intrinsic properties via changing the synthetic conditions. [4] In the study of glucose derived hard carbons the synthetic method consists of initial hydrothermal carbonisation of glucose followed by high temperature pyrolysis. Previously, only the pyrolysis temperature was varied in the synthetic process. This showed for example that increasing the pyrolysis temperature results in greater pore size which is beneficial for sodium storage but decreases the defect concentration and interlayer spacing which is detrimental to sodium storage. [2] Exploring the large space of remaining possible experimental conditions in this synthetic process is investigated using a data-driven approach with the goal of increasing the efficiency at which optimal conditions are found. A closed loop Bayesian optimisation workflow for investigating synthetic conditions to obtain hard carbons with improved electrochemical performance is being developed. The workflow consists of training a surrogate model on a dataset developed from archived experimental data to make predictions on new synthetic conditions. Based on these predictions the next experimental condition is selected for investigation. The results from the synthesis and characterisation at the suggested conditions are then collected and processed in an automated way to update the dataset and the surrogate model. A custom toolkit for the automated analysis and extraction of important features from both the electrochemical and microstructure characterisation is implemented. Furthermore, the use of a research data management system in the developed process allows for the effective integration of data from the experimental workflow with the computational workflow. The developed process provides a more efficient and data-driven approach to experimental work in the field of hard carbon materials for SIBs.