Machine learning generalised DFT+ U projectors in a numerical atom-centred orbital framework Article Swipe

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Amit Chaudhari , Kushagra Agrawal , Andrew J. Logsdail ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1039/d5dd00292c

We present machine learning-based workflows using symbolic regression and support vector machines to simultaneously optimise Hubbard U values and projectors, enabling accurate and efficient simulations of defects and polarons in complex metal oxides.

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Type: article
Language: en
Landing Page: https://doi.org/10.1039/d5dd00292c
PDF: https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00292c
OA Status: diamond
References: 99
OpenAlex ID: https://openalex.org/W4415696392

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DOI: https://doi.org/10.1039/d5dd00292c

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Title: Machine learning generalised DFT+ U projectors in a numerical atom-centred orbital framework

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-01-01

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Authors: Amit Chaudhari, Kushagra Agrawal, Andrew J. Logsdail

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Landing page: https://doi.org/10.1039/d5dd00292c

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PDF URL: https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00292c

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