We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as re- fitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking.
Supporting Information for Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE