Physics

Cluster (spacecraft)

Mathematics

Algorithm

Smoothness

Statistical physics

Potential energy surface

Force field (fiction)

Extrapolation

Geodesy

Mathematical analysis

Benchmark (surveying)

Artificial intelligence

Geography

Programming language

Cluster expansion

Molecule

Computer science

Quantum mechanics

Potential energy

Classical mechanics

Thermodynamics

Journal of Chemical Theory and Computation

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.

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