New open-shell CCSDTQ implementation in CFOUR applied to basis set convergence of post-CCSDT(Q) corrections on W4-08 dataset, yielding good agreement for ozone electron affinity.
Karton , author E
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Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
ADC-G3W2 reformulates vertex corrections to the GW self-energy as nonperturbative resummations within the ADC framework to guarantee positive semi-definiteness of the self-energy.
FNO truncation at 0.0025-0.001 cutoffs plus extrapolation from {0.005,0.0025} approximates quintuple contributions in CCSDTQ(5) for thermochemistry, with slower convergence for second-row compounds.
citing papers explorer
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A new open-shell CCSDTQ implementation and its application to the basis set convergence of post-CCSDT(Q) corrections in computational thermochemistry
New open-shell CCSDTQ implementation in CFOUR applied to basis set convergence of post-CCSDT(Q) corrections on W4-08 dataset, yielding good agreement for ozone electron affinity.
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Accurate and scalable exchange-correlation with deep learning
Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
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An Algebraic-Diagrammatic Construction for Vertex Corrections to the $GW$ Self-Energy
ADC-G3W2 reformulates vertex corrections to the GW self-energy as nonperturbative resummations within the ADC framework to guarantee positive semi-definiteness of the self-energy.
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FNO-CCSDTQ(5)$_\Lambda$ as an economical alternative for connected quintuple excitations contributions in coupled cluster thermochemistry
FNO truncation at 0.0025-0.001 cutoffs plus extrapolation from {0.005,0.0025} approximates quintuple contributions in CCSDTQ(5) for thermochemistry, with slower convergence for second-row compounds.
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