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Reducing potential energy surface grid points by up to 81% without sacrificing accuracy could revolutionize computational efficiency in molecular simulations.
Foundation machine learning interatomic potentials are poised to disrupt computational chemistry by offering quantum accuracy at force-field speeds, potentially replacing DFT as the go-to method.
