AI Article Synopsis
- The authors present a new machine learning workflow designed to predict how defects affect the Raman response of 2D materials.
- By integrating various techniques, including machine-learned potentials and a density of states method, they can simulate large systems with tens of thousands of atoms.
- They validate their approach by applying it to isotopic graphene and defective hexagonal boron nitride, finding their predictions align well with experimental data, suggesting potential for further studies in solid-state physics.
Article Abstract
We introduce a machine learning prediction workflow to study the impact of defects on the Raman response of 2D materials. By combining the use of machine-learned interatomic potentials, the Raman-active Γ-weighted density of states method and splitting configurations in independant patches, we are able to reach simulation sizes in the tens of thousands of atoms, with diagonalization now being the main bottleneck of the simulation. We apply the method to two systems, isotopic graphene and defective hexagonal boron nitride, and compare our predicted Raman response to experimental results, with good agreement. Our method opens up many possibilities for future studies of Raman response in solid-state physics.
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| http://dx.doi.org/10.1088/1361-648X/ada106 | DOI Listing |
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