Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
Journal:
arXiv
Published Date:
Jul 9, 2025
Abstract
We investigate Machine-Learned Force Fields (MLFFs) trained on approximate
Density Functional Theory (DFT) and Coupled Cluster (CC) level potential energy
surfaces for the carbon diamond and lithium hydride solids. We assess the
accuracy and precision of the MLFFs by calculating phonon dispersions and
vibrational densities of states (VDOS) that are compared to experiment and
reference ab initio results. To overcome limitations from long-range effects
and the lack of atomic forces in the CC training data, a delta-learning
approach based on the difference between CC and DFT results is explored.
Compared to DFT, MLFFs trained on CC theory yield higher vibrational
frequencies for optical modes, agreeing better with experiment. Furthermore,
the MLFFs are used to estimate anharmonic effects on the VDOS of lithium
hydride at the level of CC theory.
