Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session Z18: Data science, AI, and machine learning in physics II
11:30 AM–2:18 PM,
Friday, March 8, 2024
Room: M100I
Sponsoring
Units:
GDS GMED
Chair: Neha Goswami, University of Illinois Urbana-Champaign
Abstract: Z18.00007 : Structure prediction of iron hydrides across pressure range with transferable machine-learned interatomic potential*
12:42 PM–12:54 PM
Presenter:
Hossein Tahmasbi
(Center for Advanced Systems Understanding (CASUS), HZDR)
Authors:
Hossein Tahmasbi
(Center for Advanced Systems Understanding (CASUS), HZDR)
Kushal Ramakrishna
(Helmholtz Zentrum Dresden-Rossendorf)
Mani Lokamani
(Helmholtz-Zentrum Dresden-Rossendorf)
Attila Cangi
(Helmholtz Zentrum Dresden-Rossendorf)
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).
*This work was partially supported by the Center for Advanced Systems Understanding (CASUS) which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon state government out of the State budget approved by the Saxon State Parliament.Computations were performed on a Bull Cluster at the Center for Information Services and High-Performance Computing (ZIH) at Technische Universit"at Dresden and on the cluster Hemera of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR).
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