) framework for machine-learning interatomic potentials

While ab initio methods are vital for predicting the properties of materials and simulating chemical processes, the trade-off between predictive accuracy and computational efficiency hinders their application to large systems and long simulation times. We present the Ultra-Fast Force Fields (UF3) framework for machine-learning interatomic potentials that are as fast as the fastest traditional empirical potentials, sufficiently accurate for applications, and physically interpretable. Using a cubic B-spline basis and linear regression with second-order regularization, these effective two- and three-body potentials are fast to both evaluate and fit, requiring little human parametrization effort. For data from density functional theory, the predicted energies, forces, phonon spectra, and elastic constants closely match those of the reference method. Finally, we benchmark the UF3 framework using elemental systems and demonstrate its application to multi-component systems.