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Abstract
The development of automated computational tools is required to accelerate the discovery of novel battery
materials. In this work, we design and implement a workflow, in the framework of Density Functional
Theory, which autonomously identifies materials to be used as intercalation electrodes in batteries, based
on descriptors like adsorption energies and diffusion barriers. A substantial acceleration for the calculations
of the kinetic properties is obtained due to a recent implementation of the Nudged Elastic Bands (NEB)
method, which takes into consideration the symmetries of the system to reduce the number of images to
calculate. We have applied this workflow to discover new cathode materials for Mg-ion batteries, where
two of these materials display a threefold increase in the potential of the Chevrel phase, the state-of-the-art
cathode in Mg-ion batteries.
materials. In this work, we design and implement a workflow, in the framework of Density Functional
Theory, which autonomously identifies materials to be used as intercalation electrodes in batteries, based
on descriptors like adsorption energies and diffusion barriers. A substantial acceleration for the calculations
of the kinetic properties is obtained due to a recent implementation of the Nudged Elastic Bands (NEB)
method, which takes into consideration the symmetries of the system to reduce the number of images to
calculate. We have applied this workflow to discover new cathode materials for Mg-ion batteries, where
two of these materials display a threefold increase in the potential of the Chevrel phase, the state-of-the-art
cathode in Mg-ion batteries.
