MRS Meetings and Events


MD01.04.03 2023 MRS Spring Meeting

Deep Learning Prediction and Optimization of Photon Upconversion in Lanthanide-Doped Nanoparticles

When and Where

Apr 11, 2023
2:15pm - 2:30pm

Marriott Marquis, Second Level, Foothill C



Eric Sivonxay1,Xiaojing Xia1,Emory Chan1,Samuel Blau1

Lawrence Berkeley National Laboratory1


Eric Sivonxay1,Xiaojing Xia1,Emory Chan1,Samuel Blau1

Lawrence Berkeley National Laboratory1
Deep learning prediction and optimization of photon upconversion in Lanthanide-doped nanoparticles<br/>Eric Sivonxay, Samuel M. Blau<br/><br/>Lanthanide-doped upconverting nanoparticles (UCNPs) have unique optical properties, capable of near-infrared excitation to yield visible and ultraviolet emissions. UCNPs have broad applications ranging from biosensing and drug delivery to super-resolution microscopy. Factors affecting the nonlinear photophysical properties of UCNPs include size, shape, doping, shell thickness, and surface ligands, defining a vast chemical design space. Due to the complexity of the energy transfer networks formed by the 4f electron shells of the lanthanide dopants, rationally designing UCNPs with high upconverting efficiency for tailored applications remains unfeasible. While Monte Carlo simulations allow for reasonably accurate in silico prediction of optical properties, calculations scale poorly with particle size and dopant loading, constraining the search for UCNPs with desirable properties to be fundamentally Edisonian. Here, we report efforts to overcome these challenges by combining high-throughput data generation with nanoparticle representation learning. We combine a high-performance implementation of lanthanide energy transfer Monte Carlo with automated workflows and HPC resources to generate the first large dataset of over 5,000 simulated spectra for UCNPs. We then develop a novel representation of nanoparticle features, enabling deep learning of spectra. Finally, we apply these models to optimize UCNP heterostructure, targeting high UV emission for 3D printing, and present preliminary results.

Symposium Organizers

Mathieu Bauchy, University of California, Los Angeles
Ekin Dogus Cubuk, Google
Grace Gu, University of California, Berkeley
N M Anoop Krishnan, Indian Institute of Technology Delhi

Symposium Support

Patterns and Matter, Cell Press

Publishing Alliance

MRS publishes with Springer Nature