• Tue. Mar 5th, 2024

Revolutionizing Metal Oxide Particle Synthesis: Machine Learning Optimizes Process Parameters and Predicts Properties


Feb 12, 2024
Creating data science methods for producing nanoparticles

Researchers from PNNL have developed a new approach to synthesize targeted particles of materials using data science and machine learning (ML) techniques. Traditionally, researchers have relied on intuition or trial-and-error methods, which are inefficient and time-consuming. However, this new approach addresses two main issues: identifying feasible experimental conditions and foreseeing potential particle characteristics for a given set of synthetic parameters.

The ML model developed by the researchers can predict potential particle size and phase for a set of experimental conditions, 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 economize the time and effort expended on ad hoc iterative synthesis approaches.

The study by Juejing Liu et al., titled “Machine learning assisted phase and size-controlled synthesis of iron oxide particles,” demonstrates the accuracy of the approach. By training the ML model on careful experimental characterization, the study revealed that pressure applied during the synthesis plays a crucial role in determining the resulting phase and particle size. The search and ranking algorithm used in the study also uncovered previously overlooked importance of this parameter.

The study can be found in the Chemical Engineering Journal (2023) with DOI 10.1016/j.cej.2023.145216. This new approach to synthesize targeted particles using data science and machine learning techniques is a game-changer in metal oxide particle synthesis, paving the way for more efficient and effective particle engineering applications.

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