Investigating the Hydroxyl Reorientation in Hydroxyapatite Using Machine Learning Potentials

Abstract

The chain or network of hydroxyl groups (OH–) is crucial in determining the structure and function of materials, especially in hydroxyapatite (HAP), a mineral essential for human bones. HAP exhibits a linear arrangement of OH– along the c-axis, which determines its phase transition, dielectric, and piezoelectric properties. However, the mechanism underlying OH– reorientation with temperature remains elusive using traditional experimental and theoretical methods. To address this, we developed a machine learning atomistic potential for HAP using an active learning algorithm, which achieved density functional theory-level accuracy in describing OH– of HAP. The machine learning molecular dynamics simulations revealed that the reorientation of OH– in HAP with temperature occurs through ″flip-flop″ motion, rather than proton transfer. This process starts at about 473 K and accelerates with increasing temperature, consistent with the experimentally observed transformation from the monoclinic to hexagonal phase. At 973 K and above, the rapid “flip-flop” reorientation process leads to an undetermined orientation of OH– along the c-axis. These findings highlight the potential of machine learning-accelerated molecular dynamics simulations in unraveling the microscopic mechanisms underlying the hydrogen bond network in complex multicomponent materials at the atomic level.