Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization

Xiaojing Xia; Eric Sivonxay; Brett A Helms; Samuel M Blau; Emory M Chan

doi:10.1021/acs.nanolett.3c03568

Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization

Nano Lett. 2023 Dec 13;23(23):11129-11136. doi: 10.1021/acs.nanolett.3c03568. Epub 2023 Dec 1.

Authors

Xiaojing Xia¹, Eric Sivonxay², Brett A Helms¹, Samuel M Blau², Emory M Chan¹

Affiliations

¹ The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
² Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.

PMID: 38038194
DOI: 10.1021/acs.nanolett.3c03568

Abstract

The photon upconverting properties of lanthanide-doped nanoparticles drive their applications in imaging, optoelectronics, and additive manufacturing. To maximize their brightness, these upconverting nanoparticles (UCNPs) are often synthesized as core/shell heterostructures. However, the large numbers of compositional and structural parameters in multishell heterostructures make optimizing optical properties challenging. Here, we demonstrate the use of Bayesian optimization (BO) to learn the structure and design rules for multishell UCNPs with bright ultraviolet and violet emission. We leverage an automated workflow that iteratively recommends candidate UCNP structures and then simulates their emission spectra using kinetic Monte Carlo. Yb³⁺/Er³⁺- and Yb³⁺/Er³⁺/Tm³⁺-codoped UCNP nanostructures optimized with this BO workflow achieve 10- and 110-fold brighter emission within 22 and 40 iterations, respectively. This workflow can be expanded to structures with higher compositional and structural complexity, accelerating the discovery of novel UCNPs while domain-specific knowledge is being developed.

Keywords: Bayesian optimization; kinetic Monte Carlo; machine learning; upconverting nanoparticle.