Researchers at PNNL have revolutionized the process of synthesizing targeted particles of materials by using data science and machine learning (ML) techniques. This innovative approach, which is detailed in a study published in the Chemical Engineering Journal, streamlines synthesis development for iron oxide particles.
The researchers’ approach aimed to address two main issues: identifying feasible experimental conditions and predicting potential particle characteristics for a given set of synthetic parameters. The ML model they developed can predict the potential size and phase of iron oxide particles based on experimental conditions, helping identify promising and feasible synthesis parameters to explore.
This new approach represents a paradigm shift for 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 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 previously overlooked factors such as pressure applied during the synthesis that affect particle size and phase.
Juejing Liu et al’s study, “Machine learning assisted phase and size-controlled synthesis of iron oxide particles,” highlights how this innovative technique can transform metal oxide particle synthesis. It can be found in the Chemical Engineering Journal (2023) with DOI: 10.1016/j.cej.2023.145216