Reshma’s paper on benchmarking the computational predictions of migration barriers (which influence battery rate performance), against available experimental data, over a range of battery electrodes and solid electrolytes, was published today in npj Computational Materials. The study compares the migration barrier predictions with experiments using the strongly constrained and appropriately normed (SCAN), the generalized gradient approximation (GGA), and their Hubbard U corrected frameworks in six electrodes and three solid electrolytes. Importantly, the study found that SCAN gave better predictions of migration barriers overall, with higher computational costs and convergence difficulties, while GGA’s qualitative predictions were reliable. The work was led by Prof. Sai Gautam Gopalakrishnan, in collaboration with Prof. Piero Canepa of the National University of Singapore.