  The advancement of machine learning technologies has revolutionized the
search and optimization of material properties. These algorithms often rely on
theoretical calculations, such as density functional theory (DFT), for data
inputs and validation, which are not always effective for uranium-based
materials due to their strong electron correlations. This study presents a
computationally inexpensive machine learning approach, specifically a random
forest classifier, to predict the magnetic ground states of uranium compounds
using only structural inputs. Our model, trained on a curated dataset of
experimentally-verified magnetic orders, achieves a mean accuracy of 60.2%,
significantly outperforming random chance. By excluding computationally
intensive DFT calculations, our method offers a faster and reliable alternative
for discovering new materials with desirable magnetic properties, addressing
the challenges posed by strong correlations in quantum materials.
