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Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous

Eric Sivonxay1, Lucas Attia1,2, Evan Walter Clark Spotte-Smith3

  • 1Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

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Summary
This summary is machine-generated.

Deep learning (DL) optimizes core-shell upconverting nanoparticles (UCNPs) by using a large dataset and graph neural networks. This approach identifies novel UCNP structures with significantly enhanced light emission for advanced applications.

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Chemistry

Background:

  • Deep learning (DL) applications in nanomaterial design are limited by data representation and availability.
  • Core-shell upconverting nanoparticles (UCNPs) offer promising applications in biosensing, microscopy, and 3D printing due to their unique light-emitting properties.

Purpose of the Study:

  • To overcome data limitations and leverage DL for optimizing the nonlinear optical properties of UCNPs.
  • To develop a DL framework for the inverse design of UCNPs with enhanced emission characteristics.

Main Methods:

  • Generated a large dataset (>6,000) of UCNP emission spectra using kinetic Monte Carlo simulations.
  • Trained a heterogeneous graph neural network with a physically informed representation of UCNP nanostructures.
  • Employed gradient-based optimization on the trained network to discover new UCNP designs.

Main Results:

  • Identified UCNP structures with a predicted 6.5x higher emission under 800-nm illumination compared to existing ones.
  • Demonstrated the effectiveness of the DL approach in predicting and optimizing UCNP performance.
  • Established design principles for UCNP heterostructures.

Conclusions:

  • The developed DL framework successfully optimizes UCNP nonlinear optical properties.
  • This work provides a roadmap for DL-based inverse design of nanomaterials.
  • The findings enable the creation of UCNPs with superior light emission for diverse technological applications.