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Machine-learned interatomic potentials are fast becoming an indispensabletool in computational materials science. One approach is the ephemeraldata-derived potential (EDDP), which was designed to accelerate atomisticstructure prediction. The EDDP is simple and cost-efficient. It relies ontraining data generated in small unit cells and is fit using a lightweightneural network, leading to smooth interactions which exhibit the robusttransferability essential for structure prediction. Here, we present a varietyof applications of EDDPs, enabled by recent developments of the open-sourceEDDP software. New features include interfaces to phonon and molecular dynamicscodes, as well as deployment of the ensemble deviation for estimating theconfidence in EDDP predictions. Through case studies ranging from elementalcarbon and lead to the binary scandium hydride and the ternary zinc cyanide, wedemonstrate that EDDPs can be trained to cover wide ranges of pressures andstoichiometries, and used to evaluate phonons, phase diagrams, superionicity,and thermal expansion. These developments complement continued success inaccelerated structure prediction.

Developments and Further Applications of Ephemeral Data Derived Potentials