  Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the
lack of general-purpose MLPs for a broad spectrum of elements and their alloys
limits their applicability. Here, we present a feasible approach for
constructing a unified general-purpose MLP for numerous elements, demonstrated
through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve
a complete representation of the chemical space, we show, via principal
component analysis and diverse test datasets, that employing one-component and
two-component systems suffices. Our unified UNEP-v1 model exhibits superior
performance across various physical properties compared to a widely used
embedded-atom method potential, while maintaining remarkable efficiency. We
demonstrate our approach's effectiveness through reproducing experimentally
observed chemical order and stable phases, and large-scale simulations of
plasticity and primary radiation damage in MoTaVW alloys. This work represents
a significant leap towards a unified general-purpose MLP encompassing the
periodic table, with profound implications for materials science.
