An external magnetic field induces momentum-selective symmetry breaking in CsV3Sb5's electronic structure, consistent with piezomagnetism originating from vanadium Van Hove singularities at the charge density wave onset.
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Perdew, Kieron Burke, and Matthias Ernzerhof
8 Pith papers cite this work, alongside 14,387 external citations. Polarity classification is still indexing.
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MLIP Studio unifies 60+ universal machine learning interatomic potentials in a free web platform for interactive atomistic simulations, benchmarking, and MLIP-accelerated DFT workflows.
Presents a Moreau-Yosida regularized inversion framework in periodic Sobolev spaces to recover Kohn-Sham exchange-correlation potentials via proximal mapping and limiting procedure.
Orbital-optimized DFT with plane waves yields useful absorption intensities for single-configuration Rydberg states but fails for multi-configurational states, with median errors of ~29% vs ~189%.
Plane-wave orbital-optimized DFT gives reliable dipole moments for diffuse Rydberg states where atom-centered basis sets fail, and PBE0 is the best functional tested.
First-principles calculations predict zero or negative linear compressibility in six MCN phases (M = Ag, Au, Cu) arising from a 'bamboo forest' geometry of rigid 1D chains with sparse packing.
Boron doping broadens the stability window of Cu2O and Cu4O3 phases in reactive-sputtered Cu-O films, delaying the shift to CuO and enabling resistivities as low as 0.06 Ω cm in mixed-valence regimes.
Equivariant MPNNs (MACE, PaiNN, SO3Net) outperform invariant ones (SchNet, FieldSchNet) in transferability and spectral accuracy for IR spectroscopy of organic molecules while maintaining high fidelity on training data.
citing papers explorer
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Magnetic field-induced momentum-dependent symmetry breaking in a kagome superconductor
An external magnetic field induces momentum-selective symmetry breaking in CsV3Sb5's electronic structure, consistent with piezomagnetism originating from vanadium Van Hove singularities at the charge density wave onset.
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MLIP Studio: An Open Platform for Interactive Benchmarking and Atomistic Simulations Using Machine Learning Interatomic Potentials
MLIP Studio unifies 60+ universal machine learning interatomic potentials in a free web platform for interactive atomistic simulations, benchmarking, and MLIP-accelerated DFT workflows.
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Moreau-Yosida-based Kohn-Sham Inversion for Periodic Systems
Presents a Moreau-Yosida regularized inversion framework in periodic Sobolev spaces to recover Kohn-Sham exchange-correlation potentials via proximal mapping and limiting procedure.
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Excited-state Properties Beyond the Excitation Energy from Orbital-Optimized Density Functional Calculations II: Absorption Spectra
Orbital-optimized DFT with plane waves yields useful absorption intensities for single-configuration Rydberg states but fails for multi-configurational states, with median errors of ~29% vs ~189%.
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Excited-state Properties Beyond the Excitation Energy from Orbital-Optimized Density Functional Calculations I: Dipole Moments of Rydberg States
Plane-wave orbital-optimized DFT gives reliable dipole moments for diffuse Rydberg states where atom-centered basis sets fail, and PBE0 is the best functional tested.
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Negative and Zero Linear Compressibility in MCN (M = Ag, Au, Cu): A First-Principles Study
First-principles calculations predict zero or negative linear compressibility in six MCN phases (M = Ag, Au, Cu) arising from a 'bamboo forest' geometry of rigid 1D chains with sparse packing.
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Boron-assisted stabilization of low-resistivity mixed-valence Cu-O thin films prepared by reactive magnetron sputtering
Boron doping broadens the stability window of Cu2O and Cu4O3 phases in reactive-sputtered Cu-O films, delaying the shift to CuO and enabling resistivities as low as 0.06 Ω cm in mixed-valence regimes.
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Benchmarking machine-learned interatomic potentials for molecular infrared spectroscopy
Equivariant MPNNs (MACE, PaiNN, SO3Net) outperform invariant ones (SchNet, FieldSchNet) in transferability and spectral accuracy for IR spectroscopy of organic molecules while maintaining high fidelity on training data.