Machine Learning Speeds Up Iron Melting Curve Calculations for Earth's Core

This paper addresses a significant challenge in geophysics: accurately modeling the melting behavior of iron under the extreme pressure and temperature conditions found at Earth's inner core boundary. The authors overcome the computational cost of DFT+DMFT calculations, which are crucial for capturing electronic correlations, by developing a machine-learning accelerator. This allows for more efficient simulations and ultimately provides a more reliable prediction of iron's melting temperature, a key parameter for understanding Earth's internal structure and dynamics.

• •Developed a machine-learning accelerator to speed up DFT+DMFT calculations.
• •Achieved a 2-4 times reduction in DMFT iterations.
• •Predicted the melting temperature of iron at Earth's core conditions.
• •Provides a more accurate understanding of Earth's inner core.