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Machine-learned potentials for eucryptite: A systematic comparison

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  • FOCUS ISSUE: Machine-learned Potentials in Materials Research
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Abstract

Three machine-learned potentials (SNAP, NNP, ACE) were created from the same training set of DFT energies and forces for a total of 1024 structures. DFT calculations were performed with the PBE functional and the Grimme D3 corrections. DFT energies can be reproduced within a few meV by the potentials. The potentials are evaluated how they predict structures, thermal expansion coefficients, and ionic conductivities of α- and β-eucryptite. Structures and thermal expansion coefficients are in good agreement with experimental values. All potentials reproduce the negative thermal expansion coefficient along the c axis of β-eucryptite, although only ACE calculates a negative thermal expansion coefficient for the volume. Ionic conductivities can be predicted only qualitatively correct. Molecular dynamics simulations performed with some of the potentials at higher temperatures can result in unphysical structures.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The software used for this work is available from Materials Design Inc. (http://www.materialsdesign.com).

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Authors and Affiliations

  1. Materials Design SARL, 42, Avenue Verdier, 92120, Montrouge, France

    Jörg-Rüdiger Hill

  2. SCHOTT AG, Hattenbergstrasse 10, 55122, Mainz, Germany

    Wolfgang Mannstadt

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  1. Jörg-Rüdiger Hill
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  2. Wolfgang Mannstadt
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by J-RH. The first draft of the manuscript was written by J-RH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jörg-Rüdiger Hill.

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Jörg-Rüdiger Hill is an employee and shareholder of Materials Design Inc. which distributes the MedeA software environment used for this work.

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Hill, JR., Mannstadt, W. Machine-learned potentials for eucryptite: A systematic comparison. Journal of Materials Research 38, 5188–5197 (2023). https://doi.org/10.1557/s43578-023-01183-7

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