LATTE: an atomic environment descriptor based on Cartesian tensor contractions
This work addresses the need for efficient and scalable descriptors in machine learning models for interatomic potentials, with incremental improvements in flexibility and performance.
The authors tackled the problem of constructing interatomic potentials by proposing LATTE, a new atomic environment descriptor based on Cartesian tensor contractions, which is shown to be competitive with fast potentials and extensible to near state-of-the-art accuracy.
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.