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Can machines learn density functionals? Past, present, and future of ML in DFT

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arxiv 2503.01709 v1 pith:CF7S6O6P submitted 2025-03-03 physics.comp-ph cond-mat.mtrl-sciphysics.chem-ph

classification physics.comp-phcond-mat.mtrl-sciphysics.chem-ph
keywords densitydifferentfunctionalfuturemanyappliedapproachesapproximations
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Density functional theory has become the world's favorite electronic structure method, and is routinely applied to both materials and molecules. Here, we review recent attempts to use modern machine-learning to improve density functional approximations. Many different researchers have tried many different approaches, but some common themes and lessons have emerged. We discuss these trends and where they might bring us in the future.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Machine learning potentials for modeling alloys across compositions

    cond-mat.mtrl-sci 2025-06 conditional novelty 7.0 of 10

    Motif-based sampling of training configurations produces machine learning potentials that accurately predict alloy properties across compositions, as validated against experiments for phase diagrams, melting, short-ra...

  2. ML and AI for density functional theory: different priorities for Kohn-Sham and orbital-free DFT, for electronic and nuclear DFT

    physics.chem-ph 2026-07 accept novelty 5.0 of 10

    Deep neural nets remain useful for KS DFT XC functionals, but KEFs and nuclear DFT favor lighter models and symbolic regression because of stricter accuracy and cost constraints.

  3. Quantum statistical mechanics: Gauge invariance, operator shifting, hyperdensity functionals, and nonequilibrium sum rules

    cond-mat.stat-mech 2026-05 unverdicted novelty 5.0 of 10

    Operator shifting, defined by commutation with the local current, is an exact gauge symmetry of quantum statistical mechanics and yields a family of force, hyperforce, product, two-body, and nonequilibrium sum rules.

  4. Future directions in nuclear $\beta$ decay at FRIB and beyond

    nucl-th 2026-07 unverdicted

    A community white paper summarizing the current state and future directions of nuclear beta-decay studies at FRIB, with no new quantitative result.

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