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Data-driven methods have transformed the prospects of the computational chemical sciences, with machine-learned interatomic potentials (MLIPs) speeding up calculations by several orders of magnitude. I reflect on theory-driven, as opposed to data-driven, discovery based on
Chris J. Pickard (Tue,) studied this question.