  The controllable synthesis of iron oxides particles is a critical issue for
materials science, energy storage, biomedical applications, environmental
science, and earth science. However, synthesis of iron oxides with desired
phase and size are still a time-consuming and trial-and-error process. This
study presents solutions for two fundamental challenges in materials synthesis:
predicting the outcome of a synthesis from specified reaction parameters and
correlating sets of parameters to obtain products with desired outcomes. Four
machine learning algorithms, including random forest, logistic regression,
support vector machine, and k-nearest neighbor, were trained to predict the
phase and particle size of iron oxide based on experimental conditions. Among
the models, random forest exhibited the best performance, achieving 96% and 81%
accuracy when predicting the phase and size of iron oxides in the test dataset.
Premutation feature importance analysis shows that most models (except logistic
regression) rely on known features such as precursor concentration, pH, and
temperature to predict the phases from synthesis conditions. The robustness of
the random forest models was further verified by comparing prediction and
experimental results based on 24 randomly generated methods in additive and
non-additive systems not included in the datasets. The predictions of product
phase and particle size from the models are in good agreement with the
experimental results. Additionally, a searching and ranking algorithm was
developed to recommend potential synthesis parameters for obtaining iron oxide
products with desired phase and particle size from previous studies in the
dataset.
