Evaluation of foundational machine learned interatomic potentials for migration barrier predictions

Digital Discovery 2026 Open access

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Authors: Achinthya Krishna Bheemaguli, Penghao Xiao, Gopalakrishnan Sai Gautam
DOI: 10.1039/d5dd00534e
Cited by: 3
References: 77

Abstract

We benchmark foundational MLIPs against DFT-NEB calculated migration barriers and the corresponding accuracy of intermediate geometries. We also highlight the possible correlation between the accuracy of barrier and geometry predictions.

Fields of Study & Subjects

Machine Learning in Materials Science Advanced Battery Materials and Technologies Inorganic Chemistry and Materials

Publication details

Journal / source
Digital Discovery
Publisher
Royal Society of Chemistry (RSC)
Volume
5
Issue
4
Pages
1809–1819
Publication date
1 January 2026
Language
EN