arXiv:2507.06929v2 Announce Type: replace-cross 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
The continuous advancements in AI and computational materials science are enabling the training of sophisticated machine learning models on high-fidelity quantum chemistry data, making this research timely.
This development allows for highly accurate simulations of material properties, like phonon dispersions, at significantly reduced computational cost, accelerating materials discovery and design in various industries.
The ability to accurately model lattice dynamics using MLFFs at Coupled Cluster level accuracy means materials research can move faster from theoretical prediction to experimental validation and application.
- · Materials science researchers
- · Pharmaceutical industry
- · Advanced manufacturing
- · Computational chemistry software developers
- · Traditional high-cost ab initio simulation methods
Accelerated discovery of novel materials with optimized properties for energy storage, semiconductors, or catalysts.
Reduced R&D cycles and costs in industries reliant on new material development, potentially leading to faster market entry for innovative products.
The democratization of advanced materials simulations, enabling smaller research groups or companies to compete with well-funded institutions and fostering innovation upstream.
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