Volume 2 Issue 1
March  2022
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Juefan Wang, Abhishek A Panchal, Pieremanuele Canepa. Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes[J]. Materials Futures, 2023, 2(1): 015101. doi: 10.1088/2752-5724/acb506
Citation: Juefan Wang, Abhishek A Panchal, Pieremanuele Canepa. Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes[J]. Materials Futures, 2023, 2(1): 015101. doi: 10.1088/2752-5724/acb506
Paper •

Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes

© 2023 The Author(s). Published by IOP Publishing Ltd on behalf of the Songshan Lake Materials Laboratory
Materials Futures, Volume 2, Number 1
  • Received Date: 2022-11-27
  • Accepted Date: 2023-01-16
  • Publish Date: 2023-02-09
  • Ion transport in materials is routinely probed through several experimental techniques, which introduce variability in reported ionic diffusivities and conductivities. The computational prediction of ionic diffusivities and conductivities helps in identifying good ionic conductors, and suitable solid electrolytes (SEs), thus establishing firm structure-property relationships. Machine-learned potentials are an attractive strategy to extend the capabilities of accurate ab initio molecular dynamics (AIMD) to longer simulations for larger systems, enabling the study of ion transport at lower temperatures. However, machine-learned potentials being in their infancy, critical assessments of their predicting capabilities are rare. Here, we identified the main factors controlling the quality of a machine-learning potential based on the moment tensor potential formulation, when applied to the properties of ion transport in ionic conductors, such as SEs. Our results underline the importance of high-quality and diverse training sets required to fit moment tensor potentials. We highlight the importance of considering intrinsic defects which may occur in SEs. We demonstrate the limitations posed by short-timescale and high-temperature AIMD simulations to predict the room-temperature properties of materials.
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