  Structure prediction has become a key task of the modern atomistic sciences,
and depends on the rapid and reliable computation of the energy landscape.
First principles density functional based calculations are highly reliable,
faithfully describing the entire energy landscape. They are, however,
computationally intensive and slow compared to interatomic potentials. Great
progress has been made in the development of machine learning, or data derived,
potentials, which promise to describe the entire energy landscape at first
principles quality. However, compared to first principles approaches, their
preparation can be time consuming and delay searching. Ab initio random
structure searching (AIRSS) is a straightforward and powerful approach to
structure prediction, based on the stochastic generation of sensible initial
structures, and their repeated local optimisation. Here, a scheme, compatible
with AIRSS, for the rapid construction of disposable, or ephemeral, data
derived potentials (EDDPs) is described. These potentials are constructed using
a homogeneous, separable manybody environment vector, and iterative neural
network fits, sparsely combined through non-negative least squares. The
approach is first tested on methane, boron nitride, elemental boron and urea.
In the case of boron, an EDDP generated using data from small unit cells is
used to rediscover the complex $\gamma$-boron structure without recourse to
symmetry or fragments. Finally, an EDDP generated for silane (SiH$_4$) at 500
GPa enables the discovery of an extremely complex, dense, structure which
significantly modifies silane's high pressure phase diagram. This has
implications for the theoretical exploration for high temperature
superconductivity in the dense hydrides, which have so far largely depended on
searches in smaller unit cells.
