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

12 October 2021, Version 4
This content is a preprint and has not undergone peer review at the time of posting.


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 4 or 5-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 datasets, but 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 dataset comprising a potential energy surface of a flexible drug-like molecule.


Atomic Cluster Expansion
Force Field
Machine Learning
Machine Learning Force Field
molecular mechanics
Interatomic potential
molecular modelling
molecular dynamics

Supplementary materials

Supporting Information for Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Supporting Information for the manuscript.
3BPA benchmark
This folder contains the training and test sets for the 3BPA table in the manuscript and the data to reproduce the dihedral plots.


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