Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE

We demonstrate that
fast and accurate linear force fields can be
built for molecules using the atomic cluster expansion (ACE) framework.
The ACE models parametrize the potential energy surface in terms of
body-ordered symmetric polynomials making the functional form reminiscent
of traditional molecular mechanics force fields. We show that the
four- or five-body ACE force fields improve on the accuracy of the
empirical force fields by up to a factor of 10, reaching the accuracy
typical of recently proposed machine-learning-based approaches. We
not only show state of the art accuracy and speed on the widely used
MD17 and ISO17 benchmark data sets, but we also go beyond RMSE by
comparing a number of ML and empirical force fields to ACE on more
important tasks such as normal-mode prediction, high-temperature molecular
dynamics, dihedral torsional profile prediction, and even bond breaking.
We also demonstrate the smoothness, transferability, and extrapolation
capabilities of ACE on a new challenging benchmark data set comprised
of a potential energy surface of a flexible druglike molecule.