Researchers from PNNL have developed a new approach to streamline synthesis development for iron oxide particles using data science and machine learning (ML) techniques. This innovative approach addresses two main issues: identifying feasible experimental conditions and foreseeing potential particle characteristics for a given set of synthetic parameters.
The ML model they developed can predict potential particle size and phase for a set of experimental conditions, helping identify promising and feasible synthesis parameters to explore. By training the ML model on careful experimental characterization, the approach demonstrated remarkable accuracy in predicting iron oxide outcomes based on synthesis reaction parameters. The study, “Machine learning assisted phase and size-controlled synthesis of iron oxide particles” by Juejing Liu et al., was published in the Chemical Engineering Journal (2023) with the DOI: 10.1016/j.cej.2023.145216.
This new approach has the potential to significantly economize the time and effort expended on ad hoc iterative synthesis approaches by providing researchers with a more efficient way to identify optimal synthetic conditions for targeted iron oxide particles. Moreover, the search and ranking algorithm used in this study revealed previously overlooked importance of pressure applied during the synthesis on resulting phase and particle size, which will help researchers optimize their experiments more effectively in future studies.