Comparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes

TL;DR

This study benchmarks high-dimensional neural network potentials against systematic molecular fragmentation for all-trans alkanes, showing HDNNs achieve higher accuracy in predicting potential energies compared to fragmentation methods.

Contribution

It provides a direct comparison of HDNN potentials and fragmentation methods for large alkanes, demonstrating the superior accuracy of HDNNs in energy predictions.

Findings

HDNN potentials have smaller errors than fragmentation methods.

Both methods produce high-quality energy predictions.

HDNNs outperform fragmentation in accuracy for large alkanes.

Abstract

Many approaches, which have been developed to express the potential energy of large systems, exploit the locality of the atomic interactions. A prominent example are fragmentation methods, in which quantum chemical calculations are carried out for overlapping small fragments of a given molecule that are then combined in a second step to yield the system's total energy. Here we compare the accuracy of the systematic molecular fragmentation approach with the performance of high-dimensional neural network (HDNN) potentials introduced by Behler and Parrinello. HDNN potentials are similar in spirit to the fragmentation approach in that the total energy is constructed as a sum of environment-dependent atomic energies, which are derived indirectly from electronic structure calculations. As a benchmark set we use all-trans alkanes containing up to eleven carbon atoms at the coupled cluster level of theory. These molecules have been chosen because they allow to extrapolate reliable reference energies for very long chains, enabling an assessment of the energies obtained by both methods for alkanes including up to 10 000 carbon atoms. We find that both methods predict high-quality energies with the HDNN potentials yielding smaller errors with respect to the coupled cluster reference.