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Structure prediction of iron hydrides across pressure range with transferable machine-learned interatomic potential

Tahmasbi, H.; Ramakrishna, K.; Lokamani, M.; Cangi, A.


Recently, machine-learned interatomic potentials (ML-IAPs) have emerged as a solution to the computational limitations of density functional theory (DFT)-based approaches, enabling the modeling of large systems with hundreds or even thousands of atoms. Here, we demonstrate the efficacy of automated and systematic methods for training and validating transferable ML-IAPs through global optimization techniques.

We utilize the PyFLAME code [1] to construct a highly transferable neural network potential. With this accurate and fast potential, we systematically investigate the potential energy surfaces (PESs) of FeH through global sampling using the minima hopping method [2] over a wide range of pressures. This comprehensive exploration enables us to predict stable and metastable iron hydrides from 0 to 100 GPa.

Our analysis reveals the experimentally observed global minimum structures -the dhcp, hcp, and fcc phases- in agreement with previous studies. Furthermore, our exploration of the PESs of FeH at various pressures uncovers numerous interesting modifications and stacking faults of the aforementioned phases, including several remarkably low-enthalpy structures.

This investigation led to the discovery of a rich array of novel stoichiometric crystal phases of FeH across a wide pressure range, confirming the presence of coexisting regions containing known FeH structures. This finding demonstrates one of the benefits of using large-scale structure prediction techniques to uncover the PESs of materials.

[1] H. Mirhosseini, H. Tahmasbi, S. R. Kuchana, S. A. Ghasemi, and T. D. Kühne, Comput. Mater. Sci. 197, 110567 (2021).

[2] M. Amsler and S. Goedecker, J. Chem. Phys. 133, 224104 (2010).

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