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, as well as a charge aware MLFF approach 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.
| Comments: | 17 pages, 7 figures |
| Subjects: | Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Computational Physics (physics.comp-ph) |
| Cite as: | arXiv:2507.06929 [cond-mat.mtrl-sci] |
| (or arXiv:2507.06929v2 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2507.06929 arXiv-issued DOI via DataCite |
From: Sita Schoenbauer [view email]
[v1]
Wed, 9 Jul 2025 15:11:55 UTC (5,640 KB)
[v2]
Wed, 20 May 2026 11:43:28 UTC (3,187 KB)
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