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

19 July 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

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

Supplementary materials

Title
Description
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Title
Supporting Information for Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Description
Supporting Information for the manuscript.
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Title
3BPA benchmark
Description
This contains the training and test sets for the 3BPA table in the manuscript.
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