Electronic structure articles from across Nature Portfolio

By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.

Compositionally complex alloys have attracted significant attention recently, but the role of electronic correlations in these materials is unknown. Redka et al. study the CrMnFeCoNi alloy using a combination of experimental and theoretical techniques, revealing strong correlation effects far from the Fermi edge.

By developing a machine learning framework, a recent study substantially accelerates the calculation of electron–phonon coupling, making it computationally feasible to predict and understand a range of important physical phenomena, including electronic transport, hot-carrier relaxation, and superconductivity in complex materials.

The discovery of passivating agents for perovskite photovoltaics can be an arduous and time-consuming process. Now, a machine-learning model is reported that accelerates the selection of bifunctional pseudo-halide passivators. The identified pseudo-halide passivators were experimentally shown to enhance the performance of perovskite solar cells.

Two computational methods — one physics-based, and the other one deep-learning based — are proposed to enable the systematic investigation of magnetic order in moiré magnets from first principles.

A rotational and time-reversal equivariant neural network designed to represent the spin–orbital density functional theory Hamiltonian as a function of the atomic and magnetic structure enables ab initio electronic-structure calculations of magnetic superstructures. These calculations can efficiently and accurately predict subtle magnetic effects in various chemical environments.