arXiv Machine Learning June 30, 2026 By Manasa Kaniselvan, Alexander Maeder, Denghui Lu, Alexandros Nikolaos Ziogas, Mathieu Luisier

MALOQ: Massively Accelerated Learning of Operators for Quantum Transport

Read the original on arXiv Machine Learning →

arXiv:2606. 28911v1 Announce Type: new Abstract: Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously unfeasible scales.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

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arXiv Machine Learning

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arXiv Machine Learning

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