Dynamical pseudopotentials with sum-over-poles representation reproduce all-electron scattering over wide energy ranges and enable a consistent many-body treatment of all-electron atoms, pseudo-atoms, and solids.
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Bayesian active learning with SSCHA predicts phase transitions in materials like CsPbI3 using only 50-256 first-principles calculations.
The work identifies two distinct topological phases in bond-alternating spin-1 nanographene chains and proposes two specific molecular candidates whose phases can be distinguished by inelastic electron tunneling spectroscopy.
citing papers explorer
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Dynamical pseudopotentials
Dynamical pseudopotentials with sum-over-poles representation reproduce all-electron scattering over wide energy ranges and enable a consistent many-body treatment of all-electron atoms, pseudo-atoms, and solids.
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Predicting challenging phase transitions with Bayesian active learning
Bayesian active learning with SSCHA predicts phase transitions in materials like CsPbI3 using only 50-256 first-principles calculations.
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Two topological phases in exchange alternating spin-1 nanographene chains
The work identifies two distinct topological phases in bond-alternating spin-1 nanographene chains and proposes two specific molecular candidates whose phases can be distinguished by inelastic electron tunneling spectroscopy.