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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.06929 |
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| _version_ | 1866913146864664576 |
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| author | Schönbauer, Sita Carbone, Johanna P. Eriksson, Fredrik V. Libisch, Florian Grüneis, Andreas |
| author_facet | Schönbauer, Sita Carbone, Johanna P. Eriksson, Fredrik V. Libisch, Florian Grüneis, Andreas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy Schönbauer, Sita Carbone, Johanna P. Eriksson, Fredrik V. Libisch, Florian Grüneis, Andreas Materials Science Machine Learning Computational Physics 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. |
| title | Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy |
| topic | Materials Science Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2507.06929 |