Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules

TL;DR

This paper introduces a method to significantly reduce the complexity of global machine learning force fields for large molecules, enabling linear scaling and maintaining high accuracy in molecular simulations.

Contribution

The authors develop an automated approach to reduce interatomic descriptor features, preserving accuracy and improving efficiency in global MLFFs, applicable to various models.

Findings

Non-local features are essential for accuracy in large molecules.

Reduced descriptors have a number of features comparable to local ones.

The approach enables linear scaling of MLFF computational cost.

Abstract

Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors in kernel methods (or a number of parameters in neural networks) to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF; however, our methodology can be equally applied to other models. We found that non-local features (atoms separated by as far as 15$~\r{A}$ in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5$~\r{A}$). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.