Researchers from PNNL have revolutionized the synthesis of targeted particles of materials by developing a new approach that utilizes data science and machine learning (ML) techniques. This streamlined synthesis development for iron oxide particles is detailed in a study published in the Chemical Engineering Journal.
The researchers addressed two main issues: identifying feasible experimental conditions and predicting potential particle characteristics based on a given set of synthetic parameters. Their ML model can predict iron oxide particle size and phase, helping identify promising and feasible synthesis parameters to explore.
This innovative approach represents a paradigm shift for metal oxide particle synthesis and has the potential to significantly reduce the time and effort expended on ad hoc iterative synthesis approaches. By training the ML model on careful experimental characterization, the approach demonstrated remarkable accuracy in predicting iron oxide outcomes based on synthesis reaction parameters. Additionally, the search and ranking algorithm used revealed the previously overlooked importance of pressure applied during the synthesis on the resulting phase and particle size.
For more information, Juejing Liu et al’s study, “Machine learning assisted phase and size-controlled synthesis of iron oxide particles,” can be found in the Chemical Engineering Journal (2023) with the DOI: 10.1016/j.cej.2023.145216.