Kohn–Sham equations
PulseAugur coverage of Kohn–Sham equations — every cluster mentioning Kohn–Sham equations across labs, papers, and developer communities, ranked by signal.
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New training signal improves machine-learned Hamiltonian models for DFT
Researchers have developed a new training signal called cumulative local density of states (cumulative LDOS) to improve machine-learned Hamiltonian models used in density functional theory (DFT). This method addresses l…
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New method improves AI-generated chemical physics functionals
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 functi…
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Neural operators learn Kohn-Sham map for faster DFT calculations
Researchers have developed a novel approach to density functional theory (DFT) by using neural operators to learn the Kohn-Sham map, which bypasses the computationally intensive orbital diagonalization step. This method…
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Machine learning for DFTB: Transferability of functionals analyzed
A new research paper investigates the transferability of machine-learned exchange-correlation functionals to density-functional tight binding (DFTB) methods. The study reveals that improvements in machine-learned functi…
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AI research reveals low dimensionality explains redundancy in materials data
Researchers have discovered significant redundancy within electronic structure datasets used for machine learning in materials science. They found that the underlying data possesses low intrinsic dimensionality, meaning…
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New EFT approach reconciles KS eigenvalues with photoemission data
Researchers have developed an effective field theory to explain discrepancies between calculated and measured electronic band structures in certain metals. Their work shows that Kohn-Sham eigenvalues can represent quasi…