ArticleJournal of chemical theory and computation2026
Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy.
Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy.Journal of chemical theory and computation · 2026Article
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5 authors.
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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 experimental 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 the experiment. Furthermore, the MLFFs are used to estimate anharmonic effects on the VDOS of lithium hydride at the level of the CC theory.
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Registered trials
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