In the world of metal oxide particle synthesis, researchers have long relied on trial-and-error methods or intuition to achieve their desired outcomes. However, a team of researchers from PNNL has developed a new approach that leverages data science and machine learning (ML) techniques to streamline the process and make it more efficient.
Their innovative method addresses two main challenges: identifying feasible experimental conditions and predicting potential particle characteristics based on synthetic parameters. By using an ML model, they can accurately predict the size and phase of iron oxide particles for a given set of experimental conditions, making it easier to explore promising and feasible synthesis parameters.
This study represents a significant paradigm shift in metal oxide particle synthesis, with the potential to significantly reduce the time and effort required for ad hoc iterative approaches. The ML model was trained using careful experimental characterization, demonstrating remarkable accuracy in predicting outcomes based on synthesis reaction parameters. Additionally, the search and ranking algorithm used revealed the importance of pressure applied during synthesis on the resulting phase and particle size.
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.