Active Learning-Guided Polymorph Control in Co-Precipitation Synthesis.

Phase control of polymorphic materials is important yet challenging due to the vast synthesis parameter space and the sensitivity of certain phases to specific conditions. Recent advances in integrating artificial intelligence with laboratory automation offer promising solutions to address experimentation challenges like this. In this work, we developed an active learning-guided robotic synthesis workflow to achieve phase control during co-precipitation synthesis. This workflow was demonstrated using FeC2O4·2H2O, a polymorphic compound with diverse applications. The optimal synthesis conditions for obtaining pure α-FeC2O4·2H2O were identified using Bayesian optimization. Building on this, an active learning-guided workflow that can predict phase outcomes based on given synthesis parameters was showcased, enabling more efficient exploration of selective synthesis. The influence of synthesis parameters on the morphology of FeC2O4·2H2O was also preliminarily examined. This study highlights how artificial intelligence with robotic synthesis can accelerate the uncovering of synthesis-phase relationships and advance controllable material synthesis.