Enhancing transferability of machine learning-based polarizability models in condensed-phase systems via atomic polarizability constraint
Abstract Accurate prediction of molecular polarizability is essential for understanding electrical, optical, and dielectric properties of materials. Traditional quantum mechanical (QM) methods, though precise for small systems, are computationally prohibitive for large-scale systems. In this work, w...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01705-3 |
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