Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions

Achinthya Krishna Bheemaguli; Penghao Xiao; Gopalakrishnan Sai Gautam

doi:10.1039/d5dd00534e

January 1, 2025Digital DiscoveryOpen Access

Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions

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Key Points

The research focuses on assessing machine-learned interatomic potentials for predicting ionic migration barriers in battery materials.
Utilized machine learning algorithms to model interatomic potentials.
Conducted computational evaluations of migration barriers.
Compared predictions with existing methods to assess accuracy and efficiency.
Machine-learned potentials significantly reduced computational time while maintaining accuracy.
Enhanced predictive capability for ionic migration barriers was achieved.
Results suggest potential for optimizing next-generation battery materials.

Implication

Evaluation demonstrates improved predictions of ionic migration barriers in battery materials, suggesting advancements in material design.

Abstract

Fast, and accurate prediction of ionic migration barriers (Em) is crucial for designing next-generation battery materials that combine high energy density with facile ion transport. Given the computational costs associated...

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Cite This Study

Bheemaguli et al. (2025) studied this question.

synapsesocial.com/papers/69ccb63f16edfba7beb87f4a https://doi.org/10.1039/d5dd00534e

View Full Paper

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Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions

Key Points

Implication

Abstract

Discussion

Cite This Study

Evaluation of Foundational Machine Learned Interatomic Potentials for Migration Barrier Predictions

Key Points

Implication

Abstract

Discussion

Cite This Study

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