Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales...

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Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

K Noordhoek, C Bartel - Nanoscale, 2024 - pubs.rsc.org
The surface properties of solid-state materials often dictate their functionality, especially for
applications where nanoscale effects become important. The relevant surface (s) and their …
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Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook

P Xue, R Qiu, C Peng, Z Peng, K Ding… - Advanced …, 2024 - Wiley Online Library
The application of machine learning (ML) techniques in the lithium battery field is relatively
new and holds great potential for discovering new materials, optimizing electrochemical …
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Vibrational Entropy and Free Energy of Solid Lithium using Covariance of Atomic Displacements Enabled by Machine Learning

MK Phuthi, Y Huang, M Widom… - ar** machine learning models for crystal property predictions has been hampered by
the need for labeled data from costly experiments or Density Functional Theory (DFT) …
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