  We propose a new descriptor for local atomic environments, to be used in
combination with machine learning models for the construction of interatomic
potentials. The Local Atomic Tensors Trainable Expansion (LATTE) allows for the
efficient construction of a variable number of many-body terms with learnable
parameters, resulting in a descriptor that is efficient, expressive, and can be
scaled to suit different accuracy and computational cost requirements. We
compare this new descriptor to existing ones on several systems, showing it to
be competitive with very fast potentials at one end of the spectrum, and
extensible to an accuracy close to the state of the art.
