Deep learning interatomic potential for Ca-O system at high pressure
FL Wu and F Zheng and WB He and XR Cao and TY Lu and ZZ Zhu and SQ Wu, PHYSICAL REVIEW MATERIALS, 6, 103802 (2022).
Calcium-containing oxides are fundamental components of the Earth's crust and mantle. Analysis of their structural behavior contributes to our understanding of the Earth's interior, which needs a reliable interatomic potential. Here, we present an interatomic potential for the Ca-O system using the deep neural network method. The initial training data set for the deep interatomic potential (DP) consists of snapshots of prototype binary structures from materials projects and crystal- derived structures based on Ca-O binary compounds, as well as their molecular dynamic trajectories. The accuracy of the DP is evaluated by predicting forces and energies in comparison with those from ab initio calculations. We demonstrate that the vibrational and thermodynamic properties based on DP calculations are in excellent agreement with those from ab initio calculations. Besides, we also construct a temperature-pressure phase diagram of Ca-O compounds with DP at a lower cost compared to ab initio methods. Finally, we use the DP to explore Ca-O structures by combining it with the genetic algorithm, the accuracy of which is validated by a principal component analysis for the local atomic environments. The DP training procedure used in this work is equally applicable to other systems for accurate atomistic simulations as an effective method.
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