Matter at Extreme Conditions: III

Serpentines are layered hydrous magnesium silicates (MgO·SiO₂·H₂O) formed through the hydration of peridotite, a geochemical process that significantly alters the physical property of this mantle rock. They are challenging to investigate experimentally and computationally due to the complexity of natural serpentine samples and the number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on the r²SCAN meta-GGA functional. We illustrate the success of this ML potential model in reproducing the high-temperature equation of states of several hydrous phases under the Earth's subduction zone conditions, including brucite, lizardite, and antigorite. In addition, we investigate the polysomes of antigorite with periodicity m = 13-24, which is believed to include all the naturally existent antigorite forms. We found that antigorite with m larger than 21 appears more stable than lizardite at low temperatures. This machine learning potential can be further applied to investigate more complex antigorite superstructures with multiple coexisting periodic structures.