Creating data science methods for producing nanoparticles

Researchers at PNNL have revolutionized the process of synthesizing targeted particles of materials by utilizing data science and machine learning (ML) techniques. In a study published in the Chemical Engineering Journal, researchers have developed a new approach that streamlines synthesis development for iron oxide particles, addressing two main issues: identifying feasible experimental conditions and predicting potential particle characteristics for a given set of synthetic parameters.

The ML model developed by the researchers can accurately predict potential particle size and phase based on experimental conditions, helping identify promising and feasible synthesis parameters to explore. This innovative approach represents a significant 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.

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 importance of pressure applied during the synthesis on 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.

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