We introduce a compact cluster expansion method constructed over Jacobi and Legendre polynomials to generate highly accurate and flexible machine-learning force fields. The constituent many-body contributions are separated, interpretable, and adaptable to replicate the physical knowledge of the system. In fact, the flexibility introduced by the use of the Jacobi polynomials allows us to impose, in a natural way, constraints and symmetries to the cluster expansion. This has the effect of reducing the number of parameters needed for the fit and of enforcing desired behaviors of the potential. For instance, we show that our Jacobi-Legendre cluster expansion can be designed to generate potentials with a repulsive tail at short interatomic distances, without the need of imposing any external function. Our method is here continuously compared with available machine-learning potential schemes, such as the atomic cluster expansion and potentials built over the bispectrum. As an example, we construct a Jacobi-Legendre potential for carbon by training a slim and accurate model capable of describing crystalline graphite and diamond, as well as liquid and amorphous elemental carbon.

• Received 23 June 2023
• Revised 4 August 2023
• Accepted 28 August 2023

DOI:https://doi.org/10.1103/PhysRevB.108.094102