Deep learning directed synthesis of fluid ferroelectric materials

structures which are filtered by an ensemble of classifiers and regressors to identify candidates with predicted ferroelectric nematic behaviour and accessible transition temperatures. Integration with a computational retrosynthesis engine and a digitised chemical inventory narrows the design space to a synthesis-ready longlist. Eleven machine-learning-selected candidates were synthesised and characterised, with one molecule exhibiting a modulated antiferroelectric phase. Extrapolated ferroelectric nematic transitions were obtained, compared against model predictions, and used to augment the original dataset. Further iteration using graph neural networks and XGBoost models produced a second ranked set of candidates; chemical synthesis yields fluid ferroelectric materials through a practical, closed loop-workflow.