Intermolecular Non-Bonded Interactions from Machine Learning Datasets

Jia-An Chen; Sheng D Chao

doi:10.3390/molecules28237900

Intermolecular Non-Bonded Interactions from Machine Learning Datasets

Molecules. 2023 Dec 1;28(23):7900. doi: 10.3390/molecules28237900.

Authors

Jia-An Chen¹, Sheng D Chao^{1

2}

Affiliations

¹ Institute of Applied Mechanics, National Taiwan University, Taipei 106, Taiwan.
² Center for Quantum Science and Engineering, National Taiwan University, Taipei 106, Taiwan.

Abstract

Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31 was calculated by us and has been well documented, and here we use it as our training set. The CLIFF kernel type machine learning scheme is used for predicting the interaction energies of heterodimers selected from the SOFG-31 dataset. Our test results show that the overall errors are well below the chemical accuracy of about 1 kcal/mol, thus demonstrating the promising feasibility of machine learning techniques in force field modelling.

Keywords: artificial intelligence; machine learning potentials; non-bonded interactions; quantum chemistry datasets; symmetry adapted perturbation theory.

Grants and funding

This research was funded by the National Science and Technology Council of Taiwan with the grant number NSTC 112-2221-E-002-141. And The APC was funded by the National Science and Technology Council of Taiwan.