Researchers from PNNL have revolutionized the synthesis of targeted particles of materials, traditionally relying on intuition or trial-and-error methods. In a study published in the Chemical Engineering Journal, the researchers developed a new approach using data science and machine learning (ML) techniques to streamline synthesis development for iron oxide particles.
Their approach addressed two main issues: predicting feasible experimental conditions and forecasting potential particle characteristics based on synthetic parameters. The ML model they created can accurately predict iron oxide outcomes based on reaction parameters, helping identify promising synthesis parameters to explore.
This innovative method represents a paradigm shift in metal oxide particle synthesis and has the potential to significantly reduce the time and effort required for ad hoc iterative synthesis approaches. By training the ML model with careful experimental characterization, it demonstrated remarkable accuracy in predicting iron oxide outcomes. Moreover, the search and ranking algorithm used revealed the previously overlooked importance of pressure applied during synthesis on resulting phase and particle size.
Juejing Liu et al’s study titled “Machine learning assisted phase and size-controlled synthesis of iron oxide particles” is now available in the Chemical Engineering Journal (2023) with DOI: 10.1016/j.cej.2023.145216 for more information on this groundbreaking research.