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Prediction of crystallized phases of amorphous Ta2O5-based mixed oxide thin films using a density functional theory database

The genomics approach to materials, heralded by increasingly accurate density functional theory (DFT) calculations conducted on thousands of crystalline compounds, has led to accelerated material discovery and property predictions. However, so far, amorphous materials have been largely excluded from...

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Bibliographic Details
Published in:APL materials 2021-03, Vol.9 (3), p.031106-031106-8
Main Authors: Fazio, Mariana A., Yang, Le, Menoni, Carmen S.
Format: Article
Language:English
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Summary:The genomics approach to materials, heralded by increasingly accurate density functional theory (DFT) calculations conducted on thousands of crystalline compounds, has led to accelerated material discovery and property predictions. However, so far, amorphous materials have been largely excluded from this as these systems are notoriously difficult to simulate. Here, we study amorphous Ta2O5 thin films mixed with Al2O3, SiO2, Sc2O3, TiO2, ZnO, ZrO2, Nb2O5, and HfO2 to identify their crystalline structure upon post-deposition annealing in air both experimentally and with simulations. Using the Materials Project open database, phase diagrams based on DFT calculations are constructed for the mixed oxide systems and the annealing process is evaluated via grand potential diagrams with varying oxygen chemical potential. Despite employing calculations based on crystalline bulk materials, the predictions agree well with the experimentally observed crystallized phases of the amorphous thin films. In the absence of ternary phases, the dopant acts as an amorphizer agent increasing the thermal stability of Ta2O5. The least efficient amorphizer agent is found to be Nb2O5, for which the cation has similar chemical properties to those of Ta in Ta2O5. These results show that DFT calculations can be applied for the prediction of crystallized structures of annealed amorphous materials. This could pave the way for accelerated in silico material discovery and property predictions using the powerful genomic approach for amorphous oxide coatings employed in a wide range of applications such as optical coatings, energy storage, and electronic devices.
ISSN:2166-532X
2166-532X
DOI:10.1063/5.0035573