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New method improves AI-generated chemical physics functionals

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to improve machine-learned exchange-correlation functionals by incorporating stationarity constraints. This approach addresses the common issue where improved energies in these functionals come at the cost of degraded densities. By penalizing the Brillouin residual during training, which does not require a self-consistent field solution, the method enhances both energy and density accuracy for unseen molecules. AI

IMPACT This research could lead to more accurate and efficient computational chemistry simulations by improving the performance of AI-driven models.

RANK_REASON The cluster contains a research paper detailing a new method for improving machine-learned functionals in chemical physics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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New method improves AI-generated chemical physics functionals

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The cluster contains a research paper detailing a new method for improving machine-learned functionals in chemical physics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yuhao Chen, Donald G. Truhlar, Xiao He ·

    Stationarity Constraints Improve Both Energies and Densities of Machine-Learned Exchange-Correlation Functionals

    arXiv:2610.30022v1 Announce Type: cross Abstract: Empirical exchange-correlation functionals are fitted on fixed orbitals but used self-consistently, so better energies often come with worse densities. We show that the Brillouin residual of a trained functional on the Kohn-Sham o…

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