Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Olivier Malenfant-Thuot; Dounia Shaaban Kabakibo; Simon Blackburn; Bruno Rousseau; Michel Cˆot´e

doi:10.1088/1361-648x/ada106

Paper details

Research paper

Computational MLIPComputed RamanComputational DFT

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Olivier Malenfant-Thuot, Dounia Shaaban Kabakibo, Simon Blackburn, Bruno Rousseau et al.

arXiv·2025·10.1088/1361-648x/ada106·arXiv:2410.20417

Samples

Each sample groups how it was prepared, characterized, and measured. Click a card to expand.

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

1 characterization

Simulated Supercell DftCStudied Material

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

1 characterization

Simulated Supercell Dfth-BNStudied Material

Materials

graphene

Studied Material

Also known as

isotopic graphene

Variables

isotopic_variants= true

studied material

hexagonal boron nitride

h-BN

Studied Material

Why these are connected

Related documents with shared materials, methods, properties, or citations.

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning | Matter42 Literature

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Samples

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

Materials

Why these are connected

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Samples

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

Materials

Why these are connected

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Samples

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

Materials

Why these are connected

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Samples

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

Materials

Why these are connected

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Samples

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

Materials

Why these are connected

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Samples

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

Materials

Why these are connected

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Samples

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

Materials

Why these are connected

Large Scale Raman Spectrum Calculations in Defective 2D Materials using Deep Learning

Samples

Pristine and isotopically modified graphene configurations used as DFT-labeled training data and Raman workflow inputs.

Pristine and vacancy-defective hBN configurations used as DFT-labeled training data and Raman workflow inputs.

Materials

Why these are connected

Characterization

Properties

Simulations

Characterization

Properties

Simulations

Characterization

Properties

Simulations

Characterization

Properties

Simulations