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Semiconductor defects persist as a source of energy loss in solar cells. Here, the authors use ab initio simulations to reveal overlooked defect dynamics, guiding passivator design for enhanced photovoltaic performance.
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.
Dr Valentino Cooper, a Distinguished R&D Staff Member at Oak Ridge National Laboratory, talks to Nature Computational Science about his research on density functional theory and on designing high-entropy materials and piezoelectrics.